From 3bf18ed1a5e4049f80f17f6a0ed70891a82a77db Mon Sep 17 00:00:00 2001
From: Ioana Calangiu <calangiu@ini.uzh.ch>
Date: Mon, 22 Jun 2026 16:50:52 +0200
Subject: [PATCH 1/2] Add tutorial image segmentation sample notebook

---
 .../tutorial_image_segmentation_sample.ipynb  | 472 ++++++++++++++++++
 1 file changed, 472 insertions(+)
 create mode 100644 examples/tutorial_image_segmentation_sample.ipynb

diff --git a/examples/tutorial_image_segmentation_sample.ipynb b/examples/tutorial_image_segmentation_sample.ipynb
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@@ -0,0 +1,472 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PerceptionMetrics basic tutorial\n",
+    "PerceptionMetrics provides a unified evaluation pipeline for perception models, currently focused in semantic segmentation in unstructured environments. For this basic tutorial, we are going to evaluate an image segmentation model on RELLIS-3D test dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download required data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Dataset (RELLIS-3D)\n",
+    "📝 [Paper](https://arxiv.org/abs/2011.12954)\n",
+    "🧑‍💻️ [Repo](https://github.com/unmannedlab/RELLIS-3D)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting gdown\n",
+      "  Downloading gdown-6.1.0-py3-none-any.whl.metadata (8.3 kB)\n",
+      "Collecting beautifulsoup4 (from gdown)\n",
+      "  Downloading beautifulsoup4-4.15.0-py3-none-any.whl.metadata (3.8 kB)\n",
+      "Requirement already satisfied: filelock in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from gdown) (3.13.1)\n",
+      "Requirement already satisfied: requests[socks] in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from gdown) (2.32.3)\n",
+      "Requirement already satisfied: tqdm in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from gdown) (4.66.4)\n",
+      "Collecting soupsieve>=1.6.1 (from beautifulsoup4->gdown)\n",
+      "  Downloading soupsieve-2.8.4-py3-none-any.whl.metadata (4.6 kB)\n",
+      "Requirement already satisfied: typing-extensions>=4.0.0 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from beautifulsoup4->gdown) (4.12.2)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from requests[socks]->gdown) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from requests[socks]->gdown) (3.6)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from requests[socks]->gdown) (2.2.1)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from requests[socks]->gdown) (2024.6.2)\n",
+      "Collecting PySocks!=1.5.7,>=1.5.6 (from requests[socks]->gdown)\n",
+      "  Using cached PySocks-1.7.1-py3-none-any.whl.metadata (13 kB)\n",
+      "Downloading gdown-6.1.0-py3-none-any.whl (19 kB)\n",
+      "Downloading beautifulsoup4-4.15.0-py3-none-any.whl (109 kB)\n",
+      "Downloading soupsieve-2.8.4-py3-none-any.whl (37 kB)\n",
+      "Using cached PySocks-1.7.1-py3-none-any.whl (16 kB)\n",
+      "Installing collected packages: soupsieve, PySocks, beautifulsoup4, gdown\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4/4\u001b[0m [gdown]32m2/4\u001b[0m [beautifulsoup4]\n",
+      "\u001b[1A\u001b[2KSuccessfully installed PySocks-1.7.1 beautifulsoup4-4.15.0 gdown-6.1.0 soupsieve-2.8.4\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m26.1.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install gdown"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=1kV6_3NblB8JguleWJn329jrCIsNc6kQn\n",
+      "To: /Users/ioanacalangiu/Documents/GitHub/PerceptionMetrics/examples/local/data/Rellis_3D_image_split.zip\n",
+      "100%|██████████████████████████████████████████| 859/859 [00:00<00:00, 1.36MB/s]\n",
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=16KZl2eFUKG8fzRou-pOKm_a-fgUpqa_U\n",
+      "To: /Users/ioanacalangiu/Documents/GitHub/PerceptionMetrics/examples/local/data/Rellis_3D_ontology.zip\n",
+      "100%|██████████████████████████████████████| 18.4k/18.4k [00:00<00:00, 12.2MB/s]\n",
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=1De9WamNV0LclToP7ajtHhhiT7NFHW4Z7\n",
+      "To: /Users/ioanacalangiu/Documents/GitHub/PerceptionMetrics/examples/local/data/Rellis_3D_pylon_camera_node_label_id.zip\n",
+      "100%|████████████████████████████████████████| 454k/454k [00:00<00:00, 16.4MB/s]\n",
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=1Xuvi8AzI94ybe7hvWdbyqxkiCNoU4elj\n",
+      "To: /Users/ioanacalangiu/Documents/GitHub/PerceptionMetrics/examples/local/data/Rellis_3D_pylon_camera_node.zip\n",
+      "100%|██████████████████████████████████████| 23.4M/23.4M [00:00<00:00, 27.5MB/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "!mkdir -p local/data_subsample && cd local/data_subsample\n",
+    "\n",
+    "# Download\n",
+    "!gdown 1kV6_3NblB8JguleWJn329jrCIsNc6kQn -O local/data_subsample/  # split\n",
+    "!gdown 16KZl2eFUKG8fzRou-pOKm_a-fgUpqa_U -O local/data_subsample/  # ontology\n",
+    "!gdown 1De9WamNV0LclToP7ajtHhhiT7NFHW4Z7 -O local/data_subsample/  # labels\n",
+    "!gdown 1Xuvi8AzI94ybe7hvWdbyqxkiCNoU4elj -O local/data_subsample/  # images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Archive:  local/data/Rellis_3D_image_split.zip\n",
+      "  inflating: local/data/rellis3d/val.lst  \n",
+      "  inflating: local/data/rellis3d/test.lst  \n",
+      "  inflating: local/data/rellis3d/train.lst  \n",
+      "Archive:  local/data/Rellis_3D_ontology.zip\n",
+      "   creating: local/data/rellis3d/Rellis_3D_ontology/\n",
+      "  inflating: local/data/rellis3d/Rellis_3D_ontology/ontology.yaml  \n",
+      "  inflating: local/data/rellis3d/Rellis_3D_ontology/ontology.csv  \n",
+      "  inflating: local/data/rellis3d/Rellis_3D_ontology/ontology.png  \n",
+      "Archive:  local/data/Rellis_3D_pylon_camera_node_label_id.zip\n",
+      "   creating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/\n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000314-1581624684_149.png  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame001709-1581624823_650.png  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000326-1581624685_350.png  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000318-1581624684_550.png  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame001707-1581624823_450.png  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000316-1581624684_350.png  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame001713-1581624824_050.png  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame001711-1581624823_849.png  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame001715-1581624824_250.png  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000000-1581624652_750.png  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000002-1581624652_949.png  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000320-1581624684_749.png  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000322-1581624684_949.png  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000308-1581624683_550.png  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000008-1581624653_549.png  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000312-1581624683_950.png  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000324-1581624685_150.png  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000006-1581624653_349.png  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000310-1581624683_750.png  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000004-1581624653_150.png  \n",
+      "Archive:  local/data/Rellis_3D_pylon_camera_node.zip\n",
+      "   creating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/\n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000314-1581624684_149.jpg  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/frame001709-1581624823_650.jpg  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000326-1581624685_350.jpg  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000318-1581624684_550.jpg  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/frame001707-1581624823_450.jpg  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000316-1581624684_350.jpg  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/frame001713-1581624824_050.jpg  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/frame001711-1581624823_849.jpg  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/frame001715-1581624824_250.jpg  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000000-1581624652_750.jpg  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000002-1581624652_949.jpg  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000320-1581624684_749.jpg  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000322-1581624684_949.jpg  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000308-1581624683_550.jpg  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000008-1581624653_549.jpg  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000324-1581624685_150.jpg  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000312-1581624683_950.jpg  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000006-1581624653_349.jpg  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000310-1581624683_750.jpg  \n",
+      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000004-1581624653_150.jpg  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Unzip\n",
+    "!unzip -o local/data_subsample/Rellis_3D_image_split.zip -d local/data/rellis3d/\n",
+    "!unzip -o local/data_subsample/Rellis_3D_ontology.zip -d local/data/rellis3d/\n",
+    "!unzip -o local/data_subsample/Rellis_3D_pylon_camera_node_label_id.zip -d local/data/rellis3d/\n",
+    "!unzip -o local/data_subsample/Rellis_3D_pylon_camera_node.zip -d local/data/rellis3d/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model (PyTorch)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading...\n",
+      "From (original): https://drive.google.com/uc?id=1TS04wMOrfoAogVCbItsz0UGG-t3qu7g4\n",
+      "From (redirected): https://drive.google.com/uc?id=1TS04wMOrfoAogVCbItsz0UGG-t3qu7g4&confirm=t&uuid=8ff3937a-b000-4446-8962-d626e2ee21c4\n",
+      "To: /Users/ioanacalangiu/Documents/GitHub/PerceptionMetrics/examples/local/data/image_segmentation_model.pth\n",
+      "100%|████████████████████████████████████████| 159M/159M [00:08<00:00, 19.0MB/s]\n",
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=1UtVB7SN-0fE6eJLbSZHeRUJD26vJFZhK\n",
+      "To: /Users/ioanacalangiu/Documents/GitHub/PerceptionMetrics/examples/local/data/image_segmentation_model_cfg.json\n",
+      "100%|███████████████████████████████████████████| 288/288 [00:00<00:00, 419kB/s]\n",
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=1Qgoq3BlopB_zMVMbPGGlEc52aCwRZ9_n\n",
+      "To: /Users/ioanacalangiu/Documents/GitHub/PerceptionMetrics/examples/local/data/image_segmentation_model_ontology.json\n",
+      "100%|██████████████████████████████████████| 2.31k/2.31k [00:00<00:00, 4.00MB/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Download\n",
+    "!gdown 1TS04wMOrfoAogVCbItsz0UGG-t3qu7g4 -O local/data_subsample/image_segmentation_model.pth            # model\n",
+    "!gdown 1UtVB7SN-0fE6eJLbSZHeRUJD26vJFZhK -O local/data_subsample/image_segmentation_model_cfg.json       # configuration\n",
+    "!gdown 1Qgoq3BlopB_zMVMbPGGlEc52aCwRZ9_n -O local/data_subsample/image_segmentation_model_ontology.json  # ontology"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Init dataset and model objects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'perceptionmetrics'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mperceptionmetrics\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdatasets\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Rellis3DImageSegmentationDataset\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mperceptionmetrics\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mmodels\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m TorchImageSegmentationModel\n\u001b[32m      4\u001b[39m dataset = Rellis3DImageSegmentationDataset(\n\u001b[32m      5\u001b[39m     dataset_dir=\u001b[33m\"\u001b[39m\u001b[33mlocal/data/rellis3d/Rellis-3D\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m      6\u001b[39m     split_dir=\u001b[33m\"\u001b[39m\u001b[33mlocal/data/rellis3d\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m      7\u001b[39m     ontology_fname=\u001b[33m\"\u001b[39m\u001b[33mlocal/data/rellis3d/Rellis_3D_ontology/ontology.yaml\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m      8\u001b[39m )\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'perceptionmetrics'"
+     ]
+    }
+   ],
+   "source": [
+    "from perceptionmetrics.datasets import Rellis3DImageSegmentationDataset\n",
+    "from perceptionmetrics.models import TorchImageSegmentationModel\n",
+    "\n",
+    "dataset = Rellis3DImageSegmentationDataset(\n",
+    "    dataset_dir=\"local/data_subsample/rellis3d/Rellis-3D\",\n",
+    "    split_dir=\"local/data_subsample/rellis3d\",\n",
+    "    ontology_fname=\"local/data_subsample/rellis3d/Rellis_3D_ontology/ontology.yaml\",\n",
+    ")\n",
+    "\n",
+    "model = TorchImageSegmentationModel(\n",
+    "    model=\"local/data_subsample/image_segmentation_model.pth\",\n",
+    "    model_cfg=\"local/data_subsample/image_segmentation_model_cfg.json\",\n",
+    "    ontology_fname=\"local/data_subsample/image_segmentation_model_ontology.json\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ISDjrJEIjISEhISEhISEhIeGskwiNhISEhISEhISEhISzzkPCDN53kWhCoIXGR5OOdFIbTRtFB43GqCcBBNGPh8QLBH4XfE8TBgKtzH61BqVAAxvtKiL6nxAghQCpkVIgBFi2xnFAOmDZAik0AnCERgI2mhTgIAjQdBH4/bPBASzABbJIMgiyCATgR2MWG86hjmIVTVdJlNJoBFqbY9oWOFJjAxYaB0GIJgAU4CPZaMIR9NWlBgINvifwPfA9CAKNCgFhzlVgro/S5vyl1AghomtnxqCJrosEaYGM3gDH0diOQFrR9ZGaHJAFMtF5O3e53vFgdfRTC0KUNMerh4o0ipVOm8PNLItrAUqmCP0QpUJU4KNDc7wAAbqDY+fpNLrUFmepnDxCY3mNc571LLK5FPWWi1IBWgV02gHVpXmm9u3g+K2305mdwc5nyQyO4gtFc/Z2Ji96Mu2VBdxcCTc3Sn35FCNbdjB/6y0UJidpl9cQNMgND+Fmx6jMrNIoL2FlJI3lWdZOfBthCXKlLYzsewIrB+9gxxOfysr8OgM5yeLt11Odux7hhkye90TGHv08yiurrB64Dvw6wzt2oQIojEzz0Tc84z59hh4sRA3mExIeIDQIDVZ4+lOhBVLBG94H734zyIe/fVHz0DvH5B6ScP+x4fPxzrfATRfBniNw1RfgXX8I598O3308PPUa+PP/D6bn4Nf+ES6+AXYeh5FVKNYg23rY3k/O5B7ykBAa/fmnwEKTQxAisDATczPJNpNWSf+kfKARv1ZqhGWEgdag4gmtNgcQSoOOBYcwF08IhNAIqRHaTO5lNCNWSkMAOtQgzeRa2wJb0JvaKzRdwIvGIKKxZRBkEJSQuHdzrmHvR6Oixy0wgsaKBZLAlhpbGIERC4j4TbcQBAiCDYJKKyMaNqJCCAMIA41SRt4Iqc15C4FAY5kdo2V/jEiMNJLm2Co0R9YKiASZjISHLSGFJhUJIRB0o3OLRY8NpLRAAl2t8UKNtATlwMdXmqLtklaavCVpeXDpkOB6bbPchGY3xE3ZyKyDHwjCMEB5VXK5QZQSyIxg8rxzmTjnHOYO3snx7/+QQq7E8IWPJgwVdNtYIdhOyNqRk2w5Zx/H6lVEs0NjaZHUaIb11SbZmaM0mwEjNjQ6gtLkOJ5XZupR59BYX2XHJY8l9D26no8MYPqScXzvfObvvJkSGlXfTqdToVs9SXkmhUzbzHzr38lv283yKY/JCx9PemgX5UNfYOnOa/EDiZ2ZIGwukhndRnF8mqGdF2G52TP63CQkJMREN/xiDV78f+EX/nWz2NACrv1Z2HsYfuY6I0YSEhIewWh41G3wc/8Bf/n7MFAxCxBL40ZgPOZGOOcAHNwPX306eCl4wb/A//yf0SQpIeaMhYaOZuS9lef7E9E/pI4mo/Fk3sWsqIfoaNIqehN0jYkuhIASAmUBSpvJsAYVxqv2sTDoHc6s0rPhVDWgo5X8UBNGE/feyr8bRUeEeaWOpIWFxor2H3+teWis6Hjxyn0sMDzAjyI2XTReTy2aiyABhHl9PzIheuP3EXiRkApDUFqgVV8EqBC0Er296ngAGCEjLbNiJAQIaaI50upvKyQIYUasQh1FmzRYfRFjxJhGa4EKILDoRYMCjIAKo6tkRYdPoykhySFQGtY9DzfrUA00ja5HMyXIuBYzK3WsVpMtxSw7hiBf0Bw4FqK1IJeWlNe6qDCkVBjEEgIlwQ/bdKodUoU843v20jg5w/ryHPKoTXp4Gtu1KY4Osba2iOsqlm+9jWIpjecKNGnCepnhrZM0KxXqizM4Yiv5UpNaeYy04+A16ozs2o0WEqdQwlsp0/aatOfbFCeHmdj7aI7dcj37rnwJ0i0xe8M3qa+cQrfXqJZXaDcXcbPjNOZs/ECSG9lKu36YysFvMbDrMeCOki8WaNcadOseYXcFOO9ePzYJCY9sohtbrgmTC3DF1+F5n4WnfQ1cb/P3mMY8LnQySUhIeESjTWRTSXjhp+Fl/wf+92/B8Z2w7RRsP2XuF//fnxuRcf7tcNujwPEg007uH3fDfYpo9EIkGsQDFL8Uoh/dQBuRkAIkkjAao0JEk26TUiTNhghboqTGdiDwBIGvCQLQvkALjdD9NC1zdtFkWUUr/Dp+RiC0jq4DSKcfUZCYCb+JZPTHYubzgiAao0BTQ5GKjiSJU73MVfYjkRFsEEFWJH5ibaAiQaMRJv0rilgEYV9cgO6lQAkBlmXEkFab08eEACtSViJSNFIa0aDQRkQIgeMIHMdcD6UgVJHgiERNfAGVMlESbd4IfAEdR2Lb5vkgNPuWEmyhUUrTbncZyTj4QFlrHKUJXEl9rYUfKlS2iBjNMHOwTGdxHRyb4UyKLQMe3bCL0A6TEylabUEhr0mnNO2WJi0cFk61aYd10qUce664gvk77mTxpu8w+SiJNbSV1eOnGB4ZYWDHFuZvv43W4gmEnWd4bJTybIvq8QNs2f8oBqYvYeX2m3EzeVDLDOw6H1UXtNcqeL6H69g4pREytkW71WDp8AGCjoeUmsVj8xSH6+TGt9ENXRx/EGWlaS4cBr9F+vxzsH2X9e4YtrDAKrN6/A4G9z2eRrXO+EAGx3HoNNtn90OVkPCwIkqP2nHCTBSe/F9w0U0wumImEHf31SVIohgJCY9YtIl2NvKQb5gIxpefCYf3mvvGY6+HI3tM5DNejCg04JIfwqNvNSlVb343nHvnA30iD0rOWGh0MZNbK35Rz88gNkz+o2jAhvv1T0OP9KIbwvxPAO6mERgCBFmgi6aOoiNCAgtCS+DZZrLrdwA0gW8myb00rZ7o6J+MBkQY/WlF20sNUaQgiKImoYj3EqVeiX76V+ylkBgxEcsOgZnQ+2yOcqSR+FGERmwQLuHGdKhQEPqR10TH8Y0oqhKLI2EiFsjoPRH9z4zxovT9J0pps08VbRP5L4yOMGO3MIJfCE0Q+Vwk/YiIJTf4LqLxxrEXS4Bjm78HXoBAYNuCmtdB2xrHsel6mnK9xuBIgVCkjKAMQ1zHIbtvipkTi6TJMjUoSafT1ENJ2rFohYpuPsBK2dQ6Ah2GSMfFKYSU18o4eQelbcb376c0Ocbhb3wdZ36J1NQ4paFdrB9eIGXnCEa2UT12G43VeYa27iWTkdgC6vNV3BRUj9xEYes5SD+ktHOK9nqDrl8nCBQrdxwiP5AllUsT+jb5YUnBneTYd7+CFaaw81vJ2D71jiRbHKI0/kwWb/k6M9//IoN7nsDEuY+mXesg/Tpzt3+P9YPfJTc4QHv08dTXlkkXh3+iz09CwsOep38V3vcGk9oQC4hkpTEhIeGe+G/vNQsR//Fz8Iv/D8aX4B9/zUx8fv8v4eO/Dr4DKa//GoH592U/eKBG/ZDgPgqNeJIpsKMXi95k1nD6mtBP+e6+wUB8V+IxphGksKgR0kLTwXgbsAS4ZkVeYFbXTWTApATpDQcw2UCCeNE+fl6rKLLggw4xngURpQ8JjZACywYRGbZTaNKAjehFKOJrGscrVHS8jdEQ83dBqAWhNkJAhUZk6NCkSYERDLG20IooQcmgEUaMCw2WMXcTnbfxohiREAbRa5SJ6EgJlm38FpZl1IkXRUTiL3ERCQspjXekaGnyCJxI8MQRHhF5acw5QWhJWr7G74TYjqTTDhhzXdI5hyOVVVqLPlOOy0rbx8mGDDgWHVuya8c4tx84wdRwBlcKtgibAUuy1vUIXAtNiO426FCg2QoIghBL2CwdOcbQtp2EviI7MMz4eefTWprBOzVDw00zes5uWjUff0Zgy3Ea9WO01pYgO4RTmqQ78wPazUUe9fyXsXRgjvWTx/D9NulMCTdtYcsUll1mdeYAQ9NbSRdLzB68nUx+mfzACCpoUp47ir92Cu2UKIyPYjlZ9j395Zz83peYvf4LjDdXGdxxEWvlBoP7Hs3yLXPozhqzt91Mo64Z3XIesOMn+tgkJDz80MZ38Zz/hLf+qVldTMRFQkLCmXByO3zwNfCsL0I3ZVKibrgY/ujP4C/+u4mMut697yfhNM5YaPionscgNl+btKR+haSNnobY+NxLRfoRN/yNVaX6rz7tj7t5Yf81IWbiqqLtregnfn0GcLAoAVUUNRSeABGVPbJsTRjEHobIPxFFDGJhAUY4xL6O2MsQu8i1Fggdmaktc2RThakfBbCjybaCXlpUGkEm+jOHoBs914nSp3wEnpL4ft9fEqdMaa175nUZH88yokErk/alVGTOjjwYMhIUKhpLz48RiQVbaiwL0KL3mihTDYW5TjqqQuVIYxJXUfqVIzQFYBBJFhF5UUTP4B6/N2DM+qElSSufZgC6AygP2bSZyKWRk+OsVRp0vZDVEydItYewt0whbInl2Cg3x4HZFaYmx5C2B6TJOJLZShNfSQaFy5Zxm1tVg/bSCn46j50boTJzjPzEVmoLK2SKQ2ivyfSl57Fw660s3nwj7uQWtl60m0zBofWdVeYP3kAqV2D95M3UK8dwrA7NUyuM7djD7C030vXq5IcmKZQGkYOjlCZGKYxnWDl4CN06wOjWnWgnw8qt38axBbrTYmTfBbjFLVTXu9SbS4yO2Gx90i9QO3aAxuoi89/7PL5ShEojLU0YBDipKuU7v8jozql7/jAlJDwi0eD4ZjLwnjeZyjCJyEhISDgjBHziV+HCm+E1H+xHQf/oz+D6x5oFDPtuKtUlnBFCb6zp+iNY1yryHOiowpPolWntr5rraAIdT+7j8qXiXoWGpm/g3ihe4J79IPHk3wOa6J4QkoCLIIURO2E0pjjtSxGXxNU00TQAXwvCyCgdV1wKQt0THkFIlKtlVv/jFKHY+k0kOszqv0lV0rof3bCESe9yoqhQ7OWwgDwWuehaBZjyvd1ofG2gqwWeB6FvvBdCgm3HZu0oihGlTEkrKscrNGGUVqXialNx1AIRCRFz/YQwZWstCU40znh8KnpfQqQpmRuJLlfSM7nHSExVqXwkmFIY435IP+3O2rB9iElrEwhqHR9pC7yuz3q5TrGQx8k4WEqBENTbTWp1j5WVOkPTRfKDA7S6TRZmZ8jKDFNbpxjJZrCBpWqD2w8tkXEsdu+ZpNrpUO8EfP+GGZrWIEoF4Ftk8xmEYzN/4gAi8Jneu4/a4hoLhw6w94pnokLNsZtuwlYtVg7diJsfo1k5RUYqxi54On6zS311gcrcD8hvv4S0O8bQ1lGUPYCky9Lt12Flc9AJCaQi7DYY23kOuYltVOdX8VWX/MgQtfU1bBUQNBRCdFk5dRTdXqU4NUmnssr60dtRrJEp5WnVPVKZAt/47L/c8wfqQUxSmjLhp0KhBv/jHfCqj8JgOREZZ0hS3jYhYQMDZbj6JSaqIQDPgVYWStXknnIPnNXytrkNk0YXgRtFNOJrH2IqHsWTU+hXcbrbN6iXmmQEQhsz6QyIe1EI0rGY6fkO7rpDIzKqkQcjNlLL6HXpaB+CuMKTjgSS+fExk+P+3uiZoYXQOI5AuLqXhhRHD1QcVdhgHtdEEYFeKShzHnH53Dhtysb010hHxWiJUqg8NJ3edTTRDHM9pCmCYEXpTcocw3YirwO65/lQ0fmHUZoV0kQx/EBE1ar6qVLSMpWiTORJYwljUHeIU87u2tdDGUkVeTnMddO9czf/MhW/utHrIa6M1bOl98SViM51vR1iu5LQkaxXPdw0kE/RlZrltSoDGZdUNo1yXKxUSHEsw+zsMiw02L5rDGENUWmsoxdWSO/eSgh03BRTW4cAzdGTy7Q9weBogamxFAeOzuEpl3a1gZ8uMXbOVqa3TnL7N76I8H0KW3dgD6ZZuOF7jO0/h3Mv3M8d3/g6g1PbsFMpwu4gneoCtdUFXCzShRRWagBv5TCpqTRhM0voeDSXT7J15w7Sgzuol8uorKDbCqivr9Fq3kphfBeNk0uMbdlCeyBD3g5oZ1rYmTQNpVi9eY7piSG2XPZMGnMrHP3Gx+m0ZgEP36vdzQcqIeGRiob9B03Z2kRkJCQk3FcGysaH8c+/BP/9L0yJ65E1cH1wqw/06B7ynHFncBcoIChEKTEuZkIvo2hFvFodN6LLRNvA3Rfz2BgBaaFpoGiiqKOooigTskLIGooqmhZEFZviV+uomZ2p3mQiE5KGltS0YB3BMpplFC00GSAXTY/rUXO/OGrS0wZRpSTf0/jdqHmdMpN0R4JrGROzFc+Uo7EIIbAsgWODa5vUI8cy26ctTUbonsiwET1/hoM0ohnVi7B4KEJ0L/rioHGkJmND1tWk05ByTeQhHkasKPuRpb4vQ0TGazsat+1Efzf2FFxBJDLM6wKESdXClNo10Yy+Od1G46KiFLD+9RNoJKYCWDo6v9jqHntMPIygbAOt6H1voTi5VqWFRqah4ytCZeGHAeODRRwpObawRrncwEcgtM2efTtRnRpHDx7BFiGEgoWlVYQyQuzk3Dp2JkV2sMDI1iHanQZtETI4PELBapHWLVIjJdZrJ1g8fADHyZAv5tBBk7U7f0jasgmFxeFrv0YpZzO5fy+1tkL5bcZ2buOcp78A3ViiNJyhODFBac9l5Ee2Mb7vXCzXQYRtitkcE1tHOO+x02zfO8zeYYvJnGLrtjTDY0M0Fk+gvWVUbYlJe4mhAhSy0C6vMDy9lXOf+WKOf+cOykcPkkr5DOx9IvmRCxGqhN/st39MSHjEYwfw+vebZlmJyEhISPhxeOGn4TO/DOkOfPqF9+j9Tbjv3Ifytpv7V4hNd3QzxXUxk99wQ7Sjt11v5b8vFUwKk0l76qB65V3jPfb7SZgJuhOl48Sr5d1IoHTR0Sp+P7UHBEqayEQeokZxxpAcR2ZM8zxppvYCcAShNL4GKUzXbSH6JXLteNJsCUJpqloRPSeFmYRnIaooZf5MRVLAdOoWvfPuonoleINo/HG1qTghS22KCphzireJo0WhFlFjvdO3icWDFFEEI9r7RnUZeyZi0dA/vnkPwujR+L3fuL2mL27iilpudFQ/coBYxNEOCKJRBwhaWqE1FDMWBA7VhSaDQ2nsjGTAcfA8n0qzjdY2pZTN8uIKoxMTZFybooZLHr2XEyePMTFcoKwV1373IEcHBhjeMsZgKmTAsVEKXNsmlbZZWltjeynLnu3DHJ2poepN1OAY7doaYXWYwZEJxrZsYfnwAWQ2JBRFwqbLt6/+FJnBPFanwsKJU1z83OcytP0c2vMLrM+fwM4OYaezlJfLjLYqjO57DOvri+SEoF5e4MbPfZaxHcPs2LGbQtpirazIjk6zmilx47eOMHvrtQwMulxwxdPwWwIr7DI2fT5KC7Y99qmsHb6W4Sdfid8KkPkRxs6dZO3Ad0lISNiAHdz9ilZCQkLCj2JqzpSofd0HzM//7+WmP4aSYKl7f33CvXLGHo0z4a7lYGGzIIlXuH2IfkyKUCuabPsbtjOv7U9qY1+DHU1eN6ZshdEE2I4mtPFqfACkgBwmmiERtFCRgDET4ADwNXhh7MMwaUqODVkR9+joC5N4TDJ6rYom4Q6m23ecqhUbnwVGTDWiqEXfTqSjNDNzJvHf+9GDDdP+jVW9tKmSFarIgK3NWIi6cEtpRI+ILqCg3zTQXDe9oWqYiCJQoudlia9nuEFyxNfXiJ9YdIiemTw2usfvh6JfMKCftgaxzIzPzA9Cc92kJOwGlBstGm0fV0C+kKHhaxy7QzZVQKBZWFpGI5BasrBeIUDS8OtccMFF3HroTqrrNYYGhzn3vJ0sL1chsLDyLkG7Sq3WJqgrAjvAzWY4fmSJI/MrpFMZVD0At0anGzA6PkZ9ZRbFKEM7dxP6DZbvuJWVmWN0q0d4zM+9hIn9T0RIi+UDt3LH975DpqgpDowzd/udDJ33ODKDLlu3bKOzssbyyVsYmxxnZGiUtYpHbqRAqwvp1ACrK6vM3Pk9pPKwsnmG9l6CFoNYKUmt2iFcOsXiqTtIpS2anTrT27cTUCJst7n6nb/BQ5EkvzrhrGP7pgzlCz+dRDTuI4lHI+ERz9ZT8P7Xm87fb/9jU1nqN/8OxpaNObxYf6BH+KDmrHo0zgQhzER48yA23vt1z5gdRJNZtSHXP+6nHa+429Ektp++oyN/iJn0xivocXlYZ8MxPaAVpUj5wHrkYAgx3owQQaBNWdiN1aZiM3hgg04JhKUjU7OZqOeQpKNjxue2Mf1qY1zABzpR1MWIqdjp0N9S0jeHh+goASmKEvSiQFGClGZTSXgRVcUKlDGdqyAyiFv0qkvFaVHxGJ1IDMWiKDbex+M1YxBRNMI8E274VbKRveaCXiSWYLOBP/bC2MQRDx0Jnr588rUGk/XEeuhTSNkMpApkPY/AFyyUWwjbJ+3ZrK1VCJRPaEna9SaBH6Jtly2TRW67fZYffON7hKUhykttvNo8WkuUm6Vbr1FdWcNKp7CsgJRb4NiBWxkojhEGAXrhTlqpIlv27mLt2Dr11UV0pYxvOQwOdli6+RZy52xneM+5TF94Kd/5549z5Ic/xNNFHOmSyg/gpLJ47SXWWgHj5+2hsfBD9EoGd3yQwGpz3uOewNT2LdTWWoiBFnOHDnLy5AJ7H3MB+SIEXZ/SxDaUpamcnKfbPUR9fY781DidZhoKg6zN3EqhmGLb459Fp2Hjh63TP3wJCY9klsZNScpUNxEbCQkJZ85Axdw3pudMytTf/4Ypc9vIwxv/yni/knvKT8RZjWiAMUzDaYVqe8RVqWKxEUcFYiERd5OIf+JJrsCkIeWRm8rWnnZCiKiRnxEYcVpWKxIcZsU97klBryJTqMxEPS4fGxuuUy6ko5KtOST5KAqwcQDiLiPRWkembqLytP3zC4jTnvopSYo4itAXL3EqWByFENF/in7alIrHTxwtisvTmm7b8VFig7f5u6kIlbnLqOP3ZeN1NZGb/r7jlK048uFH73LYi870q4ZJ+j6ejfvU0XH8SGi2VUij06XlhYhQUiuv4qSLhH5AMW+RsdMU8ikUCi9QdDzFerXG3Mwc+VyerXumKC8uc+N3rkdkioxvn6S5skymOISTylNeqhIEAfmJAZZPrbN86mZGR3fSaDTo+k3SuSz1lWVypTFW5o4S+iGhssjkctQ7im63yt7LriCURU7d8EMW7/xPcqUhLnj6LzCy+1zmD51i6fhNhJ0mxWKRTA4WjxxheNt+znv85QhyOHaXwwdOMbF9O92Gz4nbb8HzKmghCDoh6yvz7Ni9ndLwMBU/S3N1hfrqHJniFO2gxcD0Fo5967Nc+HMvpzh9HmHX53/9/La7+e1/8JOsRiacfbTp6vs7HzL9M9KdZGJwhiQRjYRHPEIZsfHoW02jzyf/FzzmRhPZkCpKy3ygB/ng5UzuIT8VoaEwEYWN0Yl41X6jF8Cn3/fCpD6d7k7XG/az0Ydwt4iNE1pT1krRjyx4mEpOG1fuexNnbTphK2W6WMvI/J0VkEEziOyVazWHEvc4DL35f5tQGx6Nzy2edFv0+03EZWWhn5ZlRc/FV0D19qN7pWJjMRULF4Ux56eiiEK69/f+OfwocXjXx8y1hE4ULeqXFL5rBaq7XpN+2lVcmawXucGUEm43PCr1BgJFNldkoOSYaJWEMFT4nZAQOHD7HahA42QEYxNTpPJ5Qj/k2B23c+CHRxkYG6bS6vCEpz6ZTq3ND6/5MvV2l8zgMI6bgSAkPzZGUF9G6xTZtGJwahuz80ucuuE6PK/L9I49pAt5nGyKxSOz5CcnqFQCTt7wWXZd/HgaKw20EvhhjVR2hPW541iiTSadplmu0e42GJo+h3bXJ5e1CUSOlJNF2Bba6pBPO2RzA6yvtZg7cBOurJPO5xH5CVLFUYJuSHn2OPX1k5Qm91KZP8zOxz6J4Z0/g3By/PXLH3N3v3oPepJJQsJPB20qTr37zfDrH09q3p8hidBISIiJFize+Ffw2r82QqPQeKAH9aDnpyo0Nu78rj4MdH8CqjET3ziPPxYV9zyiu8YHfnx6U1/d/3d/Rb4//ng13pRy1ZEA0mQQFBA9L0M0wA2RjB9rQKeP70dsHkcZeqWE4//pjVuZczJVnDRdFN1oYm9j+olko1SpOF0qPg8T/Dn9Wmy0QMUi0aRLmZQ0EykyIsFUm9K96EeI8a+EkXCK9xGnikGcDmYIAmV+PyxBu9nC67QJvYDxiVGanQ5YNra0WF+tooRFubYOgaLT7lKrVMkVh5jeOU2+lGHm0Em+//WvUC83mNi5h4sefzlHDx/kh9d8AWnlGCgNMbTrHHTg45SGWJldZnJsgnqzjm818ZqauVu+RTad4YKn/yKT+/fQbba55Ttfo9PyOPSDa7nw6c/BcQZZWzrJ8m0/QFhp/HYFz2+QGxjCzU/QaTfpVFbID2Yp5LaSmdpBaXyc9tpJhkenmbvpOkJbkRueoN2osXh0jq0/81hKpTG6tRrri6cY3L4b32sRtDwOfe8rKFrkh3ZSGN7G/33nb93j782DmWSSkPDTQ8P2k/CvvwAX3ZSsQp4BidBISIhwu/CB15kUqve9AZ7yDdOwL/l9+5H8dIXGppf102P6Fg296Q/uuvVd7hb3lGp1tth8lj96ch9HHTZVse3xkwsNfdcHNuy7ryP0adtvjBDFciwuH9uPhMRiyXQKN00LN/c82XgCsdDw6UdJ4pQv6Jt4jHHeHKsTGdt9+p4T42HR0T5Er4qWigSPpcGNenXI3mv6jQtjb0fHC2k3fTyvietaZDI5ys0mQQAqVLR86NQ7NOs1Gs0GI5OTNNdOUV6vsWX3eYxMj3LijmPccesBwmAevSawBgdoBy2Clk+71SXtZsiNDFGa3s+hWw9gB1UGd+yi3mkztWU3J+64BW/hNjIDu5na82isQpbmygKzd9xAt3wH+bG9pAe20iovsH3/OXjhEK5b5+B3r2Pl1M3kxicZ2/WzONkcp277L4pDEwxu20/YWCfotEw38mYZpTNkRybx/WV838Wr1dl6wTnUuw5b9+5FpgrUFmfp+gG3fvXLTG7LEoQ5Vg/ezuf/8Z338ZfvwUEySUj4qeJ4ZjXybX8C2VYySbgXEqGR8MjiR800tWnM186A58Ll34avPAPS3ftzgA85zqoZ/O4qSsWr3oLYrrwhGqE3tnMzW8E93yR+2veOzce9SwQGekrErMT3xUZcpSmeFEt0z5Dd7z5x2m43nfZdRcPGdLDNvga9IePqroJGn1a9ceOVtdn4Zm6OG8XiSW04DkRmcvqd0uPeIrF3RmDM9rEIgNjjIclgokJeFAEK6FfRCqKUNEnfRB94HjLqQJ633V4Z47izuoneCBzXpl7rUG8EtIMWfrtOYdBlda3G6vwMQcfClw42inp1hdZKhcJQnqHpXawsrlFda6FCQTrrYaemOXbsu8hm3XiCPI/ilj2owGPh5EkWZtdot5oUCilkq4asr1OVkubSHDsu+Fm6rQ5ep0198QQje/ex78nP4Pp/OkbOCsiMTFAtL9DRmuEdUzipDKXja7SbK9TXV8iPn2T73iegnSezeON3GRhbITe5Fa8WsHL8OK5cYcvPXEV7vUy70kSnulgjw5y4+Tp2XvossB1CrwPaI2hXyaYCwrDN9DmPIZNKk5DwyESDFUJ498tA+A78wyth1zF49UeSFKqEhIQ+j77VpFYe3Q2fey6c2kb/PiKgOtDf9sbHwGefB7/8GZAbJl8aI0asEFLe/Tb0hzL3oeqUudBd4t4P/YlrFlPxaZM/gn7alAAsNA+m5aW702AeUI68B3FzwH61JuNtyGD6eZjKV3dJftJ9MdVTeRsm83GkIDZ5Z6PUIuNn6Vdq6okBHe0r2lXfz7LRA/GjrqlJb4pFgM+mj9SmkrRhdM5xZaq42aITpY3FFaM2mrpj30UANNA0MZW1guicddTJPKU1aSGYXTzEamWVxzzqclLSikSKisSWkataK9xiBlf7NFfa4EBlvUImW2D/Yx4HUtBq1unWPRaOKaor86wsr1AYrBEqxcJilVzaYvHkIUK/wtS+R5Me3cL67AlCv0pt8SBBw6NaXWZwfByadXRmkrnDt5MbLFBfX2V6z15aDZ/y0gKPfvpjKLSHOXHLbYxs3UFmdD/rJ28mm5/GyQ2wdOQW3PQYgZbItCRbypOfmMRfn+Hkd75OZvx8CtO7OHLtfzG6/RyyY+O4pTS2HqWxWqbZ6pAfHaG+uIjQdaz8EIWRCbq1Lum8qWzVaXk0lpcoDo7iELB1564f8Z4nJDxc0bDjhMmh/tKV8MVngbLuso2AygD8rz8wdfBf+Q+QS6q0JSQkYO4J03NGbLz+/fDz/256ZtzdPKqVNT01nvVF493wXLj9fCNQ/uPn4FUfhd/63w+mae2DljOPaNDvzJ2m30MhXk0H7vaCbzzAfQp59oIA/XX7sxIyPW2/fVpoqkAXQRiVuZXRMS1Ml26FEQjxHuJIB/RL1erIhO7R76q9sbKWG4mV+JoaU7UpbBs/Z0r39om9JaZHRd8PcXdldeN/m8aAumeEj4VTnF4VRu+fhSnhG0cvHCCNJIzGcNcqXwpNSyksaZKl+mWK+4LSEdEVkpBGk7Ic9k/tRquA1to8uZEtBKIvnCyg1Q3RGixbkskWcaazOGkbhEWn00HjoDTYUqAyMDA0QLOzn9XVRWbvPEJ+ZCtbBybIZi08GdIqL9HtuGR9j4n9OwiUYLTToL7SJrj9B9QXj2PZLvWlAC0hDB38jkXD0nRDh1a9zi31ZXZd/ixyY9OsHryBtN2h7vmU15aoriyx5Zz9rMweZnB0O5P7dtKoL5BNO3ScrbQqLbozNzD1qMcxtes3OPKd/6S71ib0LIa37SczOkh3pUx99k4cB9YXjpIdmURYpkRnc3kNO5tF+R20UKRtSbu6jHBKP/p3PCHh4Uq9AHecB3PTG/N02XTvswPYdgp2HwXHP20XCQkJDxOsAPYehgPncEYz/tvPh5dcDefeaXpmvPvN8Af/y4gNLaJ7ioBSxURE3/QeqJbgE78Kn3qRufdc/m1z/3nvf4Mrvg57jiRi4144Y4/GvW4WmQv6qVN3yYU7wzciTjPa6JWI/RLyrsf4MYhTwAJMupAbTcibwHKoqAeCwNeEgdlWRjN5IU3Z2LRlqlEJBEro3rikAEf0e3koIBT9viA2RqA50JucG6HRFyAQRxPE5jK60TVoAlXUJgGQRZKiLwY2Xq8AqNOvaBUfL0DjQc8vAZpMVFVLYARS3KTQAoqIDYJGs9yo0fDabBua6InPeL8Opk9JC0U3ercCbZoipgQMokl5HvP1FqEdYAmXfLGIKwRKA8rso4GmLRSuFNRbFfx2SK44gA4VBddhbc3HcxW265DWIYcOHKQ0vIXK+jrzSxVWq8s0K0tU5yrYWYfUyG6KoyMUBrJ0ag0O3vgdwso8IjeM9kPq6zWElAzu2sfw1F7qK2UWD1xPaXyI9bkjuEKQG57Edh2O3fp9RnfuwfddpN0lW5pi654LaVXXGd65jerCKnd+96ukMg6rM3cyuedidj/uWZSKWb7zjx8lsELcVIqRnbsJwjxOd43KyizaSdFpVDnniU8hN7ATB5/VuTXmTpykNn8zpbEh3MIWvFabf/qrN/8En4IHjiS/OqGPNvmbU/MmFWpxgnv/orirNzDKq95xAp77OSjUTfWp5/8bjK4kE4B7IfFoJDw0ie4dv/oJ2H8Q/vjtoO9as/ReXp9pm4jE73wIju+Emy80+/FdYwzfOmPuIae2wfoQnHcH/Olb4Zlfhm8+CW64GC79gSmH+wj+nTyrHg1xXz/dP+bdQGuTnmUa7W32ChS1wCbutkHvze2nLm04/KaHNl+INrCoNdVQQwiB1nS6As8D31cEQUgYapTa6ELRSCmwLYklZS+UIIVAWAJLmq7cCPP7jzBVlCxLm8Z5UpBzNI4kOoO4/4TY5AOJU5o6QIZ+xCLECKM64GmwhRmVjcLBCA8n+uKVQBEZXUdFCkExEgDt/pXGReLS70buQ6/8bQOFC2SiBn0AvgqoeU0WquvsntzW6+4OcYqV6FW/KoeCZjdqiBiC1gLb1gRZwUQqRQ7NN2/8No/a+xhKcRRHCLRlzqXb7lDudpCOxAktcoUiaIn2PQIFVipEdD26zTbZYg5pKVr1Muedt42xqQGa1QFuv6GN1fLID5VYWT1Ot1UnWJC0mzUmB0aol0ooFZAtbmGo02T18BHqMyfwK3UKE9vZe/lTWD12kOLYbmwX1mcOkC8M4boOa0fvxMmPYTkW6wcOoCtrBKkSyhLkB0aZ2LGb5UPXMzZeojF3B0e/XuE5r3wV5198Lt/75udRqTSVGY+xyW24tmLk3P2EXcXk7glOHFkm7eTIZByCTJNgIKS16DM8tY3BsR1Jw76Ehz62D+ffDk/5hjFvf+1p8JoPGo/Fj/zW3vCcDM3q4v94h/nCL9TZZGR7BH/5JyQ8rBlbhpd+0kQnfvH/3UeRASCgnTVVpm4/33QGv+1RkfcL8FJwdI/xcvz2h+EVHzMR0qF1c195+tfMT8IZcVY7g/843HV9yqQcmUdNwz0z2XcxU+giEmej2Ojt6K6q6jQHBQGmgV8ZzUIH2i1QgUYpTRAowlARBiFhGBKqSGjE4XlhRIQlBVJKRBTqkEIghUTacsN4NFoLI0xs86cU0HIFjmMEiREmAi363getoWMJLGnKxZYQvSiDDeQwUQ0PCDRYQkSlg40gM+ldpsqUSXEDB2uDkZ1eylaI6Skio8hJHIUgur4SI+4s+lESJSShsMkNDLMsoNms42SzhFogtUZpCJSg7Wk6XY3nKbSOFa/Aso3jxUuDtNPsOvdS5pcWsXN5ZCqFhTY9P3yf22aXaLfXOX/PXoYLOdoK1psdiimH2bVVyuVVdu3cz8mZoyyVXQZKU5w4dpRKtcHOc3cxM7fE1v0XUA/vYGBwnKmde6itzyKcEgevOwW6hZAZ3MEphOWSSdmM7ttHo1tBBA7dlQX8Tpvi6BQBNtWZE4zuuohuVzM9NMjJ66/BEXUca4DM7r1kJsdYnz3F4m1LpHMlOvVVHOnTXG+QLmUoDhX44j98lJRUbDv3XE7eeSd5Vac6f4rzLjqPTuCw7+KLuO2666gtLfLsl/wcrltgdWmVmVNLzB49ii1s2k2fVGIGT3ioM1CBj/86POo2Y6p0PZOG8KUrz+DF2jTSuuSHpoPvo25LREVCwiOJlVG45qmm6MPsFhhfhOWx+y44tISvPt1ENT79QrPPzz/bPDdYNubxN/4V7Dt89s/hEcQDJjTurklcnPKTQ9AGfBRt6BmOTRM7RQGJi+4VAohN55ttgf39t9DUgLqGqg+dLrTb4HmgwhCtdCQuQlSoCEON1gqt9OZqW0IQCiMQpJQIIRBCYEmJ1L3aUAiBiQNogY+Iqi0JlBL4JmcJSxpBYVumA7l5XtP0NUJAQwqqUTlYW0LBEmSEOceU0DiYlKYcIioX24+IbGxuaBN34jbiIhY1ISbdqa1N+hhC0wU8FKkooaqroas0dm97QUenWGmU8SsdqmtrjE3tRiuT9qQ1KK3RoUZr3UuDEzISN12FCizabYnvN1hbPIBWKVZvP8zEll2ks1l04FOrzHDk+LfYt/sJrHZ8qqHFwuIqwrJZIySdTjMxvZUTJ5fRbop8JkOz6zE6NcXa2jzXffMHFAZzeIEPOuTkoRsYGtuObadpVaoUBkuMjk6xtLRGq7nIvvN2cuLQKQq5DJIikz9zKeunjlOeOcroxFaWTh3Ddjusz8wjtcQaKiJSo6RHt3DO45/LyvwSmWKJdG43QxMD5ErDpHUN2Vzm8O3Xc/zALZy88xbO/ZknMnPoMN7qKik3S3l5hWy2w9EjJ9mycweVtSppt0BusMoPv/k9dp/7OBqNCkvHjyKlxlchYbfJ+JZzfsJPX0LCgwDH71eFmlyA8aV7eUF0Hx5ZNUbM13zQpF0lIiMh4ZGFlnDro+HwXpMm+Wd/ZP48eF+/G7W597zqoyZK8qHfgRf/X7j+sfC7f2v8G7nmT+EEHlk8IELDTELNyrzPxu7ScfoPtFFR87nYLmxiFB6aejQZLkQT7AYKKzJQG1OzOTGN8SjMa8W6J/A6EPiCMADf0wShiWJoZaIYSiszSVaKfklavSFYYqIrKpRorYzIEBbKzKZNGhU6EiDmeaGlySLTRFESs6dQCKSEQBo/k5TmOEphvArRNVLapGxVXEi7GtuJRIow4ivukbExnuNt+HdcqtaJoiNAdF2hHUoaLfN+uA44tumOvtLRhDqK9Kg4fUsTKo3vB5TXliiXD2Fbk2QKnnlvBAhpIS2zrRASK4rYqNBDYs490Ao/gEZthvLaQbbtfgbpTJH1tTLpRptMLks6P87Etotpey4nT1bRzipaaVJuHruQxbctTh26gUazwdjoPux8mkxB0G6GTAxuY2ikwZ03HWR6b4Ht5+2nvVrg1G0HUCisdJrBiUmmdp9DSx+iO3uKO75zHeN7doOtqbd92vOzZFRAWJLM3/oDQmeAkakpHMumNnuCysxRhO4gdECr3qI2t4gMqmy54DLWThxlcnoSiyIrszPgDLHn0mfSLi9y4AffpDC2gy3bdlFeWqRRnqG6tkppZJrluRW2nvcowmaeU4fLrK5fy+7HXEpQsZBWgMjkCGozyEybkcFLzsKnMCHhQYRUcOWX4AtXmdXK09SDNlGPp3zDpEo97nvG9J2IjISERyahZRYrPvJqyDdgeA3Ov834LVq5e3iRhlQXJhbhgltMVPTZn4fHXm/uJVtnTEWpGx8D//xLcGgfXHzj/XlWD0t+oj4asQ9DnLZd9PRGm4hgU5WQEFMStRGZm+PtrGhQHv3yuQGCTvRYvDOpYU2YJnFNBUIb/wNab0ofqvqalifwuqBDCEMdTfg1oVImcqFNcpDxVmi0MPvTUT5T326iMf1B+iIkBLSSPUEghEAqgbJAKIEUykzCRdSPW5hJuIiiHCHGSK4xk3uUidygjS9GSIltSTwNQQCWBekUpNNQxUR94u7lQGT01tHficrbgq01KWEkW0Np6p6g2VJ0OmEkfkxqmNaglUJIgW3bWFbkdNcCrTWWJbHtANd2cZwcfrdCKpXFcTJYtoVtmQiOuVQaP1RYWDRqS6TSeZSvqJZngAZuaoJM2sWxLXJpi+OHv8Hw2C4KpWEcXGZOLpGWDvkBl3y+RCqfQWFTKZdRfop0KoOQOcprNTxPsbpeplS0UEFIfniYUwcPsWXXLqan9lNZ9xFujYGBLSzOl5k9fCd+fZHW+iqtboeV62ZRvk1mdBTbF2zbu4du2CJwOzRW5siNP4bQr5AeLIBt0sbKM8eoL32MdH6U+mpAbWmZ3NRuFo6fwHEtUvlhFm/5Ln67Q3Zkip2XP52ZAweYOXyQYqmIyBXJpTLMHLmdcx73TE6dXGFy2z7G93W49dov8J3PfZad516EyIzhiDsZGh+h2crSaK7+6A9rQsKDGs1pTYGEhl/4VzixwxguvdTm7befhP/2Xvi1fzTmb0hERkLCI5lS1aRbDq9BYMOf/3/w978Bd557+rZWYLb/zb+DX/pn47co1swCx8b7iACGyma/T73m/jqThz33qY+GSZ8xk38LMxk3i/V3V3pW93P7zT+j0qeGuPRqC93rOB3HLhz66VAS0yvFCyEMhVnxD8ykVwqwbQHSjMMTUaUqDVqBCjWBDyqIDMnK/MTDiObPaAmWsMDSCGEhhEYrCHXYSwM6DWFSgqQQCBknLkWPRxN6ohKvpppS2H8+MpaHKoqAKAi16l0nKcGyLKQUWJbxYiBMpMMSkHbMPhRQR0TpTuZwfvQemb4ZgiAE348OLwRaaTzf+CeCQJl0MaVR2tjSLcvCdSwcxyKVkia1y4JQQacr6XghjjtAYShPp14x26czOLaN60pcx4xZhwotJIsLS+Cv4lguaTdHV3ZxMgK/BflsgXargp2XpByY3LKPY3fewL7zn4EQaXKlGvt37SBUIWEA7U6TQnGAlm5SGBrBTg9SWV4iWypx9NAdDAwWUeQIpcbJOXgLFW6//k4Gxoq0y4uszi9SHunSbHUoDhRotttM7NiONTTIqWPL4Hl0yjMcPXqQg9+/llRxENWtkB8d4ci1X2N4aoLC2CilyV00Gh65okWjCcp2GBgaZeXEYWYP38j8YAnlSYYnxwlDRYBFdXkG7Y8xsnsvyzd8j+WZFYZ37uGcpzyP73/2n7jz21/h+MAQU/sey45LH8vFg7/GgW/8K4GdpVNV1BtNms0h3MIQ9fX6mX9sExIeVGizCvmqj5oStDECSHfgOZ+Hq19iDJpxQu3EIrztrfDST5w+MUhISHhksv0kNHOmeV6mbfpcXP2Szb11YoFxyQ9NWtTwmvn3vd1DBKcvhiT82Jxxedu21tQwPRlsIBNVGUoBqbtsa0SEMXUHmFV2gUlrSkcG4yY6avJmfBghYlOX7FAL/MCs4gc+hLFY6CkXU3LWVHQCaZm/S8tM9JUSqBAziQ76r1cqSt1SCqU0WpnuFCYNSmBZAtu2QCsCP8QLArQyMkhEBgcptEkTkkYESMtCSGH8CdocU2uTKqTVBr+C0PSb+vWjQVprlNaR30MgbQvXljiOHY0HMilTXjdjwaCkVybXJ67OZcSaaagoe4JLKAhCCGPxpQShivwrWqNCM85QaZOSZQkcR+C6AlsEOCgKtmPS0BR0AyNUmq0WJw/fRHFoiqktO3BdiW2b3iNeN2B18QSum0ZYWRwHuh2PdlMgHONLSWfzLC3MGuu57jAyvhPXtThy5DsE9TytbpfB4QEsnUKJAMdJQSZjDNGtGiOj4wAsLi0RdurMHLmDwAtx0xm27t6NJW1CYdGtN2nVFygODLE8U6NWP0V5ZZ0tex5No9Wl2w6wnRRNrw1+hValStho01IWtuvjV6uUxncwd/hW8lNTDE/sQnXbLBw5QG7AZsdjn0ZtfY3W4iw+kqC5itYO9eVZVKjRaRdJh8HxKZpLFbwoUpXOpajMz5LOFUgXi/jdMo3VClpb7DjvMsbOuxCvssz62jrbzr2IIzfcglc+yKVXPBvhWLzlJb943z7pDxKS0pSPZLRZRfz9vzTmy7uWn9WY++P/+0XTTGthErbMmqZZT/qmMY0nvz9nlaS8bcKDH20++9CvChUvQlghPO+z8Le/a+4dj/senNzRf+nuI/DvP2/8X3HFqISzylktb7saeSaCqCJRB9Mt2o1Kp6YRvd4UcTfqDoq4EGdctjUVRSz8yCfgY8quBlqgtJmso80KvIqjGKF5LK7UFJePtS2wHOh1ydPmSDpyJscNBSWgpECoKHUJ0MIYoNWGtKjYQxEEYV+MaAXCiAwrKm/rOBaua+M6kfFbGm9D19emLK5WJiISeUBCHf9+y961IBIlxOZyjKfBcSwc28J2BPmsIG0ZcZESUECQjq57bPA2ZWVNX4wGppJU3I3dji66bfXT2CxMxS5FLOYEYSjQ0bVxbHAssFHUymvMrswyWBxkdGACB4UrJKHrkk9nyT/qURw6chJXeqTdNEprfF8jkIxM7MLzfE6dOMjo2DZS2TFC3WJpbpbB4SKN2im6fhcVtOh2l3EyQ3jdFrnCDqp+hcO3HWOv1gxPTWM7WZr1Kq3VdVr1Om6+hJvKIC0Ly7KZn58nn87RpkO33qDb7BAE0Gl2KI6NMjK5jVq1ilvMouoZ3NQEc0cPEAgHmRqkvnDESDOngCCHzICu+Vh2kaassLY0j5sboD0/y3oX3GKWsXPOZ/3YD2lUmoRhjppfIp8KWJxbxi1kkWmHsLGKblkESmBNDePmbTqVBVLpNH7LI53KIoM2zdV1/FDhhwrbDTh+y9do1pZ47FOfxsqJ47QXZ8ilWzQDD6uUhW5yt0x4qKFNFZff/DsjNPKN07/0owgwz/usqSTzd79ptr38233TeEJCwiMLoeFpX4OX/R+oDMCxXaa3xdS86fT9T79iGu5d/m1zf/mzPzJRDoRJxfzVT8Cv/JMpUztQfYBP5pHJGUc0DmkfHxNMUFHaTmzSdhFkotX1FmYCHKdNxV8P8XeKjF6jdLSv0Pxdh7HIMGlRQptKRio0EQmtxSa/RDzxd1yQtkmjMh6J2GwujCcjiiqEcSO+noCJRETk0dBKo3TYEzWgoiiDibEYA3ZUqtayTIqQlNiWSQgLVeSxwPgxzDBV1CVcI4XptQEgoxK2jmXM0lbUftyO+m04tilZO2RJ8tAzccfX049SzuI0MxcjMiroDWlnovc+WZieHAGQpR8JiQVHHEUS0b7iylQaaIcBtlasN5ssrK2TdlO0PQ+lNNl0huXqKvlMkYmpadyUAxoCX2E5kiCEeq2K12iTHRxCSoujh36ALQu022tRZDKFF0KxNE6jeoRWuU4o80g7w+TOHThSks7m8DstmtVl1tZOYdsjtGodRie3UFlco+M1KZVSyHQG7XWpLa9QmtpGbW2JVNrC1WnK6yssL51COFnChoK0y9rSUfy2wvcVoaNQfogKLJTfwslmCXyfdKZIu9vCkor62iLFgSHK6y1yWYegu4qbGiYMG+gwIFUYwmsrrFyGVrVGvlTC0oJOY4FQZwmDgGzeZvLCy+ms1xkazbJ6co5Os05+fJrFw9fRrKygdYjXCckWhhic2klxeIrJsSFu/u63OfdxFzA4ci7/49eeeyYf2wcdyWrkIxVtPBZ/9kfG1P2jfg80ZgJRLUG2Zcybye/NT4UkopHw4CXOApHm79tOwbO+aMpZv+BfTN+cVNd0BR9Zhel58G34zC+bjt6LE/Q6fcsQPvYK4/FKfn/OKmc1otGiXx0Kos7XmAltIxYPiGhiH/kUojdUR6lOSvWb2cVpPARmZV9HK/yiN2gz4Y+bvRGlFmnVN6YHWuPF31lxSpMwqSlaRIbqyCvRvzmZ9B7bMm3y0BKNQqkQpSRKGjO01jKafGukjJrWyVgoKZPShcaX8cmCJSSOI5GxIVqamIptQdaFrB1XpDLpZi6CLtoIEUSv4pZG40fRi3T0WAdNG6hG0QuiseUx5W1dYCRKZTNixKSutTDHyiDRGCGx0Wjv0u9HookiTb5HynGNXwRBM5RkcgX2FkomharTYnFulpPHTjA2up1WZR1vcAgHhSMFrhSgQ4Jui1IuRcWvU69VsK0U9ZUTdMUAqhuQLxbRqovv1WnpOtX1GqlMgcWZOSZ3bqNdW6dt2VTWT5KySnRaitLwLhzh4DWOcvKOm2jX6gxumcRXPju27GB1vUreCui2VkmRpr5eJ+U2mF04QUqETE9NsrpSob5+ip379iAtl6DdZm2tATLAdzLU1mvUF46Syg/h6ZDhsS10G8s0gxrtZkgqFZoImy0Y2TJBq9vBr1eoraxBKstAZpBMJovqdHALo+QKkvT0DtaOH6c5P8fcD75JdmCMejjAwI7dnLjhh+yZHKVQfCYrc0dpLC9SWTuJCtu0Fo/RrC0zPH4lA8Pj3PG96zj/8ukz/dgmJDx4SHeMP+NM8qMtZUyZCQkJD1/crmnSeXf9L9IduOoL8K+/AAho5E1zzxsuNo83c/Az18Hb/sT4uACcwJSnvfBmE+X4sz8yEZDf/VsjRhIeEM44onGz9npdrKG/ah4Qd5aO1vC16XLtCI1EEyKIgwQq7CcQaRVFPSJvXywEVCQuhI7X1DFRDqUjz4WJUqhYqNxl+LHA6ZnL7/qlps1jUogoWhGv4GukiErKRqlXxuitjfeAOH3KvCDUcS8MI7C8AFIO5DYYVuK0rYw0k/24WZ7G9K5QCMLI8+JjqkXZ0WgqKAYQZJB0gDqKloauMmlfZpzgWJoBYRrd5RGkI0ERK8gOJqJiRTEKH2ioEFsKQq0pCkkKSSuK3nSUptFuM5TL9lLPan6AQ4ASkBIuypI0dchSpUKnFbC8MEdhcAwhIJMtUBoo0Om0qVUrdFprBLJIp12jXlukVVtmavullBePszw3g1CacqNKxs2RKwyTy2XwvZDC0DABPpbIYMsQzwuwcPFVDdcuEATrLM6tkLEcWvU1MsVBvKaH5ZZoNhdprK+Ryw4S+hZesEg6nyFdGKRZ8Qh9H+Ha7Ni6k1Q+S2VhmaW5GWqNJfy2RGdG8FWFysxxtAbHTqGEAtfC9zSTey4il0tx/M7b2LH/UWSLBWQ2T3m5wtzRG1C1dSbPeRT1U0ew0ikGhwcJZZ7Bke2snTpCt1unvDyLWxpmdHCcbrdJbmyaRlvSaSyz6/wLufPbX6FRm0f5bTrNLkOjW7FSkkq5xZZtQ3z6wx8/k4/tg45kNfKRSlRV6q9fm/S+eBCRRDQSHlCe+E2TJrk8ZqKYI6vGl/Xdx5u+Fu95E7zyHyBwYHjVlJ3dMmua6/3G30Mra17z+WfDz1y/ed+NnCmZfeHN5r5jqQfmHB/mnNWIholYmB2aLtMiSrmJ0pSi52wBNhrXxAtQKLSI+j3YOurnYHwGZgW930vDrMRHZVSJohNRipDQmBKvKqpApQVBlH+lIw+E1fMixErDTK4tTIUpS2xMC9I4kfchrhIlEHixtsGIbB29Fm3+lJHIaPjmeSnN8UMNrch8nbN1FLEw52RH5xn3CUlFo/CITOJRVMKKrnEVTV0L6oDSCl+BFxhTt+oJJVPONyNhUAiyyF7aU0zQe4NF1HHdjCEnJGGoyAiBrTWh1HRVSMaycCSklE8q2r6Dea7tdRHCoSU8yLq0Q4mVKlJbXSA7PMUdt/8QhxS7zrsQYXc5ctt3CH0YGN/K2toCmpNk3e20tYWlM0zvPA8VOKwtl8lkBAIfy0nRaPns2ncOtpsC6ggl6VQ9vO4SbV3H73p0wwr1apvQq6IKY+x6zGVUFytkih5DQ4OUBvawvrRCtbpKrRGg2ikqlSZLB+cZm9pCq7ZMIT/J3KlD5Me3URgZpHPiEIXpSWpLAZ3aAvlilsK559FutAjI067N06oHuLZPoThEGHZJWylWZ2cZ2bkDv1IlNzLO9v2PYv3IDaweOMD4rt0E2md9ZZ52vYNfaVIrL5s0ukya6tIclZnj5AcHTRWv1RVSqTQyqHDuZU/iju9+kWZTIDohldV58oNDFEtFlk7Mn+nHNiHhAeAevni++CxTTWoq+f1NSHhEk2kZY/exXfCc/zRpTkqaf7/yH+B7jzPm7X2HTGSj4ZhqUbG365f+GW65wJS0beRNNbq7kmuaaIjr3f3zCfcbZyw00lF6jkm3MRNmQT9FJ/ZlxILBlMHVpKJJbixMOhgx4WB6YfjapAl1Npg5LCmwRWSW1pIQTaBMadtQR77vqIqSLSHtmsdtaYSOjr0JkbjIoSkie53DTYM7EQkmU2bXNPnrFakFYi+KURlxlSgNKKHxHUFZKaohZC2wU1APwJKaLKYqF9F1cjbsOxYCHWJvhHk8g+j1/nC1wgs1bS1wJRQE1C3QUlO0BJVAkZKCCRtKwvhj4vhPnNKmMJW9gujxLqbLtw5CMo5EK0UoJC2lSLmCtvLxpMlpC+gSauhqRSsMafgBLa9NwRIUMw5+19TLDbohWgVUlmdRrSXqzZD1hUFyme1MTm1heXEJr12nXl3j0RdewujoFk4cPsT6wh0Iy0IBO8/bR3PlGCdna3RbHo3lk7TGxtChQNiKVrVKu9Zl2/k7WVleZ2R4Gq/ZJD/SZeeeK5k7cZK1kwsUx6ZpN1Zw0lkClYX0MLIRgGiRGhxipNBievsE7Y7Ltj0jZIf3sLJ4gsZalcVmE7eUY+GOIwShIp0eoJgfpd2oMzQyyvxCDb8LxcEB1hYOsHbiFga2bMdxOyAFgdeiWymTll3yjoUzPsmRtVtZPno7qVKJdDpLy68ye/iHpHM5BgaHCT3B4PAwzdU1auUVjtzaQQubfC7HkevXyWaHEUqa9vWhTzqXIWhXCUJt1G1CwoOOKKc62zKG7sd9b3OJSCuEcw48cMNLSEh4cDC8ZszdH/91qBWNgLj0B/CB15nnxpdMVbqpeePFaBRMRLQQlXa3Q/jDd5mFi28+yVSzuysCGFu5P88q4R44Y6ExiBVVOjKVjaxILBQwlaZqaLpR1KEbCrQ2k34VmtX4qCcegYp7OphoRBiJB+Ub4SAtk54khPE2xOVgg7v0wIg7bBNFGcDMv2xLRH0owHEglQLLEaSitCQHI4Is+s0BA6BG3PxOYmMm67FIck8TIIJAwJglmLaiCk5S07GNoEhvcJoI+lENDRv8FXEzQiMCwugVAZARgvNt2fNRCMC3+tGYwAIneg/iS9LFiIsWmmb0XrTDfknf2BcThhA2PeMlUW0sS5K1LVPxywvJWyY81EXjCklXB3idgIx06PhdhrJpqu0WaIWLw+BggWZ5lnPPvwjP6zC/Ms+pI3UuufhScpkUS8vr5LMuq8sNbKtMrjTI7Kl5JrdOYtdX8bsdUrkixTyksjmyKR8bm7bwSamARqdJaWyYleOrFCfzHD54M149YOv+vaxX6qRKg4xnUpSX10gNDLC8Wkf7ZeZnDqE7HtJNkcmNkEo7VMpL+F1Jp9FAlluolk/YrNBqVFiaXcJOO+zeuxchMtRXG3SVT2VxAStUaN3Aa/kMDA7QLi/iWjmkzFBbnsdx0gwMD5J1bbIpB9ueZGpXm/XlFdK5ARynQXFyArGyRtAN2XrpE0nbDvXVVfL5DLd/72usrsyihUuzvE5+IM/a4ixCSixHki3maTUauG4G1axtqJSWkPAAI5RZMTzngJkgPOmbpvrLE75jViLv9jX37xATEhIeZMxugTvOM70tPvYK47V47PWw/6BZkPhv7zUei9Ayhm8wPoy4zC0YcfL+15vytTtOPCCnkXBmnLHQOOgrQi161aZklNYkMWKh6wmCIPJYBH1RoBFR9EH3BEGcjhT7KJQ2fgutIfTNZFsI04APjJ8iJjaNb+pUHnfrxjSLc2zIZwSZFORt03m8jknbyiOpRkZyR0BKm4F4QlMOYC5QphoUGleYSlgFKShGjfNsjEAJIgO3wPS0cCLzdkwc1bGiv8fn6kdRhm5k1A7oe05CjFjKSxinX21K0BcqsWixNjxn9m/8MBqNg8YX4EjR77KuQaNQYQvHShF4PpYMCJAoL0QKiQo1tWYXP0yxIwdWoLCkRbPeZHCkiLJsQq3ptts01hsMbhknl3HYtnMvaduiVmsilMvq3DyHjx1H+h7DgyPs2r6L62+8joq0KY0NYbkhUndx8kXm5spMjw3hOMvMHDjG4IRNyx8g6Gq0naEwNUUqnSE7YrG8OEthqIgzkkIHbRZnTpDPZMlm8gyNj7K+tkK3qUB5DI1OMjg0zPpymXa3ycrCDO1yGTedZbWyjuedMO+EElg2bNk1zeD4NO1am+FtW3GtZVbXA4amx6mtLPHoJzwR3Vnh2JETpB3otpqUV+ZRQYd25SS1uQNIskg7j10cwPab1BbXyaRdRHqAbrVM6AV4fhur28ALihw/MoNXWcb3PaSVJp0t0g08cvlBmipEulnQHrnBaVhZJQyaOHZAu9k4049tQsJPD7drjJe//nE4/3aT2mAHZhEpERMJCQn3hB3A078KF90EH30VdFPmsRd9ClZHTCWpv/jv5s+VUfOa5bHN+xCYpn2v/evkfvMg54yFRjOIysXGuThEk1eTS0Tom38jdK+ylMR4CWTcSpx+hSix4TdDhabykorSo0z0Q/eqVfn07Ba9g+ved5mxoUupsSRYliCbFpQKkLJNude2FrS0wBWw6ms6AT1jeRCYyIplmbKzYWgcJ7YN9VATKFgXGtc255J1BHnbpGmlhKauIStFz9OQgg19RsxPmn5KmSROyYoiGFrQ8Uz3bq1NFEa7gKXJRP6S2M8iMOLDhl4kw6Sj6ShaovBQ0RURONKkcqVs3XuMdJZWs42kixYOnUaLXMkin0kR2pqqr3GFjRQCaWm6gU8+7dNtNBFIuk6JoZFhcvksJ+ZPMjm6g1I2j+e3GR8foVh0GBgucOt1B1Gqw/DYFsItAixYLa+w1lxnbm6NhfkqrpumsnwK6lupVSvkChlymSKLp2bxvQ6F0hCjuW1kUaxXGnRbHSbGt1BpLJEfHKBabxAIBbZmfWYOLwxYm5lj657dOOkirUYXv7NCqxzQWlshCBQTW3eQKWXJ5AdoV8sIy0bjMLZ9J36rTHFoCA9JdniYvVvHKRQGOHn0do7d9n0Gc0WUqiNkjtG959KqNwmDJfLZDA2lWF9ZojSkEWFIvemRLmRZXVgitVIh1D6dTpcwDPjOF79MppQGJXHSDoHSaJ2i1WiQyxYIO13azTZ0PRxbESwdo5AfpN1QpIrDDI1vOdOPbULCT49c0zTWu+im5Is+4WGONhXTQmtz5+mEH4/ANtWiyoPwmg/C5IJ5/Jc/A3/1RvjBpSZ68c+/ZKpLAXTSp+8nue88JDhzj0bKCAETrRAopQmDeCU+cijH6U1CILQGSa/cbJzO5LqaMBQEflw5Svf8FloZcRFqBUoThCZMJqVEWpZJX+pFOUxEQdoCWwqEFKRdsG0TyVBoqi0TKfFD02NDKU0QiKhrdxQFiYSNUub3WkojmLpdgKjTN0YIIKDVUZSliLpom8yulNSkbCOI0jbkLJOiFfevSCGiDumRGTv+t9Z0PYHnQ+DrKLITiaiUSZHq1/iKU70UmchQHqd4tTGNFBWmCpgRIUaamL9HFcEQhFoinRSaEFtYZLMpVtdWSE9Po4WkE4SEboqKhhSmAV9ICksJMtkUjmOuZaXbwZFZTpw6xtDAMJ1uQOhUkUriNZsMTg5y5403kB4ZoBX4kC5iNRfptFws7bO2tMTg1BZI53CGBlk5epitU8O0qnWCtodOS5rNOoNKc/i2Izh02H/J4wiFR7ll4aYLOI0m7Uqd5tIcheIAnXKLytosa3MLphu9DnBdgZsuMD6axx2ZZnLLfgLVpVquszA3T9rOsePc7awtHmVsbBedboeO7pBLWaQzWaRuky/kyeSLdG2bwvB2MpkU3coipeE8K0tHWVqQTGwbx3ag1WzRajs4A6Nkspp2tU0+5xB6XXKhROLTVjalkSKOXUBk88hWl7W1Jfz1eYSrsV2HUbdEo+EhLGjXm4hwHSU1oUqhpPtjftwTEn4KJF/2CQ9LNGTaJlI3tG56OHzml2Fm2wM9sIcBwni5dh6HH14CV3zd3EfSHRPROLEDju80EY34BhMmAu+hyhkLjVS0Oq5tjdJRPCKqtqSjqkjdLqa5njK9L1QsHAA/MIbqwDMvDJVJS1LR0rxSEHg+vu+hVYCQAiEcLNvq97GIJv69qbPQOFKSSkWxDaXxPNNbQ2mTkmRSs0xZXt2rYhWlYBE9rxQ6jLqHxyWmouiMSdMyjfcsCUqYbuVCCqwQbEvSFoJuqE3VKwlK6ii1zIgDF9Xrip6JPCCSyJuiTPqZa9pWYElwbeNviVOmZDTSOO0qjmCkIu+I6bRuxFXcsI/ocRG9Ln6zlQVKWuRkitVqhaHCMH63TbNWIZvNYfsVslkbV9lIoN31CbUmXXDwEMysr5HNFFlea9CotiivLDF34DiedhGWMXDrsEa2tIvi9B5OHTlFSJ7MQIGFpRxeeR2hUmRHtmKnBsgKOHXHMbxqk/kAHMcmVAFZOUh+bIiF2UUyeIzs3IcfarLFIQayM6zMz9LxLLzKMpO7d+M6Rcq1kG0XXka7vEyrXsZyMkxMTZDOl6gtLVFZmMG1JqhVV2jX5ti6dTvDW8colcZwdIfxPTupNzrMHL0TXw3QrnRJux7p/CDFsV20qktM7tnD2sIchYkdwHEK4+O4tkujUiVdGmF6Ik+lWqG1uogX5gm8Bk1l7PqPeeplLM8sovwO5Wob36tjNRuQLjC1ZYI1oaiX5/DpIp00vvLJpGy0naLebhF4PlmdolnpnvEHPCEhISHhPiAUbJ0xKYFP+I7xEnz7clM1bS7pYXTWOLHDRDNe9n/gsu9Dvmkef90H4PBemNna914IZSpQAcxOm1Xh8aUkTfMhwhkLjUxUmUkDQdS3wRKCADPB9hxNOy3oBgKvE4kO3U9R0pgStSFx9+5IjGhNEHp0a02kUNi2jZKSMNBYVoAgqpBEr3AtUSc+tBC0uwrPNxP7jb6NuGmfxNSvFVF0ReneJmYckVdEKUWoTOQl7rPR31fcSFBEaWHa+DVs00cjTg9LO1B04ivWT3uKTdsK06CvjfF4pAUoF4IoZQshohK6UbQIKDmQF0ZmVTR0NXQUNDSmFK3UZISpCOZD1J/DVJiq6yiKIk3aGJjyvQKBZWXw6LLeahEKgbAtap0mtXqL0ZRgJC1Zb7ZNqd60QzqVxXZtHEswt7DE8GCRHdu30A530K6usFppodp1yuU1Zk76yFwL7Uva7QaHrr+Oqd3nkstlsbINPDuHWp6j67n4lQbaCkkV0iirQ8sPsFNZaqs1aq0Oqu2xbc9+AmFRbXnU6qs0aoJWO2RgWCKsKbAVizOHqKytYNlZBAFWuoC0M+CkaXtdBsZHWFuaodNYRoYtxqa2Y+dyCGVx9OYbyeQzLM0tkMpkcdF47SqljGJ9zccWNrWleRRtFg/eSbfTISwU6DY0KhhAZDTD0xMsHjuIlFvIZAbpplo0q2WkZeMFLTKlIQKVQ1lpvI6iMFBg7dSdDA5kSQ9N4vshVqaAqNsIIXCsFFYaglDjphSFYp5ut0W706FTqdznD3pCQkJCwr1QqphqRi/+v+C5puzq5d82RuT/94twaF+SOnXWEFAZhA++xvTCeMs7Ta+Lb18Ob3ifERaH9wLaCL9f/H/mZR/+bVOt6vn/Bn/yNlOdKhEbD2rOWGh0IyN4lCEVeQ3o9cVwotKGoWX6Q6DNpFzKjb8DutdsR/REhqLRKGM74Do5stkMYRCa7YTV94EIjY4jKXFUwljNTQqWMOJWbxAaRmyAECrqLt73OsQIHRnTox4cUgqkFVW8ipwkUkikNCZzuSF/S+vY2C6wpfGJdCMBJYWJOAigHpqJfxhFSpQSBCF0A43na+NR0RodNc1DCzNeAcuOwLbM8UIdV5CK9u9AJiNIWaYZYtPX6NBcgTASd7EIsiUIYa6V7yu6gSYIM6zV13Ecl5VGyNjIAGVvkZGwwVpHEvg+lg0q0KQdG4Qgbbts3zJOs1qntr5OIGy6YYGM49GoBZSKgwxePM3ROw5yzrn7mRz1OXDzDdSWjrD70ZfQadlYrQr4mmzahYkxFo/PIO1BCCrkR6ZIl4Y4dcdtpP0W9kCWerdLttam3ZgnZXc4eecxQr+Lv3Ub1dUKjVIeO+UyvXWareecy+LCOhnbY2XdY/ueHXSqKyzMHKPd9MkNeOy95HHU6g102GJ1ZQ2RklQXTyLcDOWFGYSlqS0vM7hnHwNDmnatzsj4IJ4q0Vhbo9P1CDsrdAmxHEl9uUwh7zA8VmJ9ZYHc8BZc16VYKqHwaZVreKHH/LED1MsLOCmX0uQ20tkS9cYqob2MtKIInJDYriDw6oSexk45hEGI8sHOFBgr2HSLSepUwoMEEUd/H+iBJCT8hKTb8L/+AH7ln8wE9pYL4PHfhc8+z/R38E2OQMJZItUxEYtmzpS1feK34Mn/Za7zn7zNCLu9h41J/GX/B6bnzHPX/izMT8FABd75FvjZa+EF/5I05HsQc8ZCo+7Fk+5ohT+KCqQkZCUIKfC0JohKLElLY2kgFCjRN3DHX0pSmrSiequGIwOGBkawLAtbapysi2VFXbQVhEr3oiBKa7SSxGlUoTLeBkEUodBmWykFjiVwbCMSwqjTt4yObUuNFia6YkzhJmpgW2DZgrSte12/BeAIjS2gqwRt35xnqIyHITatW47oVawyAkkThho/jMSDydnqj1upyKOieilaInbLayOWlI67mBszt5QCy5IIIQgVNJqKOiayYlvGB6O0SeGKU8A0gjCqcWvS1UJsy6Ropaw0XqdBxq6RckfIlCZZbHqkOzXy+QLra2sMDgzRQCK1JrAdgtDBt0KEDetry4RBijCARpCmsrpCJiOwsy5HD95JbmiCXD7H7MwMuaFh9uzZzomTa3TWZ6gqjfbqeLUAS3eZ2LsPnc5w6rZDhDpDoAPsIE06N0R5eZag61FrLGNbkmy2RBBqBidyDI9uZ3LXLrQCRZp0yqFW7uC1uqwtrNIqlymvK0QmT3ZgkLlT87Sra2SLOdCaWrmNlRmmWS9jOVBeWiHwNSvL64zu2IVfrSOERrdapIRAZi08P8RqdRgeSjFXbdGopgBBbniayvIyGQcaXY9UMY9PjlIuTX1lHjeTorJeplauki+MoHBZmZ8FGWKn8yhhY0nwtEB5a9j5IfyuMhG3jkQWXTLp0Z/kM5+QcHZoZ0xDrHMOQDpJ50t4IIgThX9SAaCNEfk5/wm/9b9N6dRnfAXe9YfQzv6kg0y4O+zAVI16zI3GB3Nih4keja7A9y8zAu/x3zXN+d70HuPR+MZT4IaLTSrK3/0m1AsmynTF12F4/YE+o4R74IyFBoFJVQqBMFSmk7UlaAmTCiSEQIWmPK0OTURAWJGxOfKKQ+SdCCEUJlTR6VQZGxhHKwVSIIXbqzAlRGQgdwQiqiqltZlU51KmHG7LN0ZtISIhJMG2Na4EN2rgZwnjKwmFJiAyh2sIhREyazVjUDeTfqNaHEvgOgKtFZaErCNwpaCtjHAIlTkvS5pGegIT1XBtI2DCqGeFiEruam1eE6eLSWmiEEpqdNyCXIvoOunIQC+jcr0SxxaR+BL91C+i2I4waVtpe0O5W23eqyCM081MFEWFAuE4pJwoOqVsZufWaHUEuVCRloKlmVMMTWxDNZrobpdUNk03NEJNaGPSF7akslomlx+ktlZGhRJfe6TyOdbnTxEC1doK6sQs6TwMjY5RXprD27qH0tA4zmCZju9Dq0qr0mRix1aqq7MoN8vwiIvX9vFaXdyUxnE1Xn4Q16rS6koGt0zSabTJDI2QSucQwkVLiy4SqQX1lodKpWn5qwSVNexAUBrJY4suk2MjuI5DdcAh48APv3MDuZECEpeluSU69Q5uSjO1ZTunDh5CaI2wJEMTYzSYJTU9wcGb76BZbyPTGVKOg5SCVrVOenAcuj6ZTAnfW8fvNvEDn069QzY9ysD4CG5mDHfAY23+FOtLS6SyDk4+RbteJWis4VhZarU6accikwG/WcaOmitaTkizE+AKjwQwKxrKdJRNVhrvfzpp+NKV8Ma/MrXuk7cg4f5mcsGYio/u+cn2k+7AM78Mf/x20wtmaRze8yYjphN+OjTz8OUr4cvPNBW9JhbNfWT3UdObZ3nMRDQ+/NumId/XnmZS2vYcMff82S0m7ern/sNENxIetJyx0AjCyIsgjIFZBSF22kYp8HyTlmP6YEQlar24mpSIJtBGOWgdzZKVpuW1cEIHtDb9BqTV31+cAoURG5YlUFEzv0BBKz5eGDeyiwzPNuRcU2bWJW62J1DCTMprKFqhoOubak9+oAkCohCNiS6gTVUoEwQw++34ulf1Kk4Hc21BJiVxrX72gG0J0j1/hhFWgRAEWtCOjO8iipL0oh4Yw7qMPSQiPidwrTj6I7CEpkv/Ob3hz1hOhGiTYqWj9DVEJHCMqBLR+9dsGd+K1oJMboKlpTvIl5pYtkvL75JrdfBFSCaXMob/UEXvv02n08ULPAI0jlKUhoaYOXSAoN3EzbsIIUhlNeG6ol5dZWmhy/SWbTRqq9z0na+TKRQZdCSB7XHo6BLDQ2PkBvLUym3OOediFo/eTD5fpLKm0YFPe2ke36tRGtvO+lKKYZnCHh1kYHAQum1KOWhW1glEima9TaADWpUFbCRu0CCdzTN39CQDwxMsL63itbpkCxkmzz2f6f2rCEswe/NBHMeBdEhleYVMKk9pcpjDN16Lmx+ntj5JENTxTlbxVQdphXRqy3itDJ5v4SgP3WnSajQhCFFCY4sUMvAJ8Wm3WuilJqO7NUGtRTpTpFAaY/7ILdiOxCkUCOtlGo1lbDdr+qGk8nTqVQIlcLMWXreDDAVuOskRBm2+kF71UfNFdHI7yUz3/kaYHOp/+hXzPmzsAp6QcH/genDhzXB0t6kQtXXGrHDf13vBxTfAo26DygD8n5fB9Y8FLe/1ZQk/LhvvFQJ815i/3/hXJkq66xj8ywvgd/+2H6nwXNNb4wnfMalsv/c38PP/DiNrD8gZJJw5Zyw0XDc2RZuJb7MbEoQ2vheY6IZjmQm4pXvlYs0Kuo5W8ePXR+4KBa1qlfGxEWzHNT6JaBKv1IYeHZgIgojSnGQkGKQES5o0JSsyYqdcQdoGL9S0AnCiSbrWZhqeFgJLSlwnqjglwLUEMmP8F0IIRDQ5l8KkN4VamG7mGqwobcySkJIC14aMjC9iv/N4Bhk17NMoAR6ajjDjcTDRFBD4WhMoGV2jyIOhTRQiCE3ak+sIUq5EYkroZkTch4PIIG/ekyCSGk50XgGxh6Tvi1FAx/OxhEUYql7lLY2LlS4yO7/M+OgoKugyd+ooA6VhiiNT1CptNBLP65AvFqlVGlipFM1qm5X5ZerlMp3aGqFuEyyHtDopxopDjE1DPp3hxK03kS2MYRdKrJyaodkMKJVS5AqSVNZCpwoEnmbXuVtYmT2Mm7IInByW3UGoBkODkk43i1cvs233BPmchZaCsF1nZaVKeucW2t2AoLlESmZI5dIUJobxGwHLS3Nk3RyB12LbjhHqHc2x2WOEnTFurX+PTK5At+VRGE6xdGqBIATXyXDqyB0Uh4exHUmrsYojA5qtNlZKgK/QykOJDF4noJTPUq+18b0ubjZFu1LGdRw6HR/HtU0lMgG16gLOnCblOggvpDA2zuSOnazMHUc1m7QabXK5LF2/gxeado1OJkuzXCPUipTjIoQi8IKf+IP/kCfdMXm8v/wZuPNcMzlIuP/ZMgtPvSYRGQkPDM0cnHunSb353b+F7z4+Ehr3ARmalKnXfQC+84SojGqyaPFT5xf+1fgtWlmTfnnTRSaS9O43m+jSB14Hn34h/M6Hosmhgl/9BLhR9ON/vMP08kl40HPGQmM4pbCEIMSkIRGE+H6DYjpNNitN9SPiBnLQ0QI/gE5b4HmmbwX0e1a0Oh0cHLSw8QOFNg03eulLPXM0JpoRdwfvRxkElqVJOQLX1mQccC3TBk9q47MwURTdiyD4QuGAybN3IGVHTfWk3hD9MF27wRjefQStSGikLXDR5BGkEabjNhoLQTs+b6Cu+1+6oYYwStvSElpRyd1AbfBxaBNlsCyBYxmvBVr2jO8pS0cpYCZSAyYtqoMpfSsRPUO8JcBGEwpTHENjfCaBMoLHtSy6XQ9pO4RBADrEdVKUsmlWFlfJuEMUchkaOmRwYph0yqHVamBJge/VWJmpksln6baadNprrMwcZWhohCAjybjT1FtruIFGdX06TR9ncBqrcIhKbRmZctiycxt3fO8HCG+QVidDfd3Dby4yMbkVV2QppGssrwuCzgza86iW57Etl47fJpMdYseeCSqrc9g5h1AJhgddaqtr1Joh+DUmtm+l3ajQ6gp2bptgceYYmUIOpMDODTKWt1gtZTj3skvxvAan7pzBtm2EzOCLFJIulZUKXtChWz+OZTt02l069SYDA4MUC0V8K4XtN+gurSBUYKJHwiHsdlFdjRYOgZII4dFptxEyRafWwHFhee4UjuPiuilS5TwdL0s2m0NaOXxl0emso0OHjhfipDQ2Ho5l4Xse6VQKoRSNtn9fP+cPM6KUqek501E2buiUcP8iQ7OCPLGYzMsS7kc2+DJaWROJeNN7THO3my/kvv0yauMRWB8y1Y6SKMb9x/ScEQv//S+MH+bX/tGkrv3wErOItD4EF9xithXadBK3TG81BqrwG//wwI094T5x5n00fIXSYLsSLTTC67A9XyCTtkhHE287Kt9aQREI44GwLI1lR6nUUd8MrTW16hrjI2M974El+1EMs34fO8eNGdqSJspAZHp2bcjbkLNMmpKZUJuIiS1Ml+8A0627KzQhglALOoFJvQqU6JWNsqSg4EJRmnMI0DQwvUFU1DXcEkaUOJFsiftZqA1lf1uAr3QvVSlGYKIgthbYkUhyLd0z1beUMP02pIlEKC1wouGFUZTE9CMR+BIcQdQvwzTpk0BBRp3YAWH6fuMRpW+hTfNEO+p74oVoYSOlhWtrLNllZDBLaz1Ad5tk8kXW108iRAihhyWhVq3hpgSV+gyZ3G6qi6coFIo4u/fieR5jpQyOzLJ7eCt33nIb1YU5yhWfTKHFyPgYzWoZVInmWpVMrkVu5Hxqq6cYGBkh66Tw6gsEo0WcTAnpzNJcXiNot1F+iNZNSsU8IgXFwWFm5+ZR9RkaHcHAwBjLJw4xMj2J1C1a9TKNlkVhIEW3GTK5azs//Ob3GdsxSa3aoTiQIgwdUmkXNzXG8NYuK4snETJLs9plbOsIbi7D2sIxPE8SSkmqlMKr1ln3FE4mjacaWDpEKEU242LZFr5fR9opdLsNyiMMJJ4XoHWA41pIxyYIFE46hyNBi4CFk7cShhIdtLDdIlqkcIolpO/RbnvkCllqq21s2yGbTbO6UiaVTiOsh/CXoRVFY7T4ybwVV37JrIJ9/tnmJ+F+RptV5D/6M2OcTUi4vxhdgd/4e5NaUx40/RT+9K2bm7udKakuvPSTxo+RiIz7EQFfuMqYuM+9Exp5eN5nYbAM//kcIx7//jfMxDF+S+3wAR1xwo/PGQuNvGvhtxTC0ygdsCedZiztRp2oNalIZIQYL0MaTcoWSDvurm0m9Z4WrDeaTI06DI+6pCPfhQK6oREZaUvjELfWM94EG7NNICCNmVBrNE7Uyq+hNR1loiIiSnsKVCRVhJmM2wLS0vSfEJEnQ2Im+VKYPhQtoK3BV+Ar09/CxVTOagTQwAxESCM+ZGRCDwEvNJ6PyNeOiMUR0NX9CIZSUQWsyAOutDZG8qhilMZEKsIwFk9RWpc0os2xTA+TOA3NSAtzteLrouinTzkiOp/QCJt8ykYqHytrofyAAEFKprCtNtXmGvligdnZNfbtUgReh2alQaW+RNrN02p5WIQMjI0Q+m2K+TRzSzVsK8fRI4dJHU6TyuXYed55rHz7myweOsrY9CRKW/iVJYJam9zYOI3mIvW1Gm4+xbbd5zA0PcrciUM4uTHwfYpDIyzOz5FKZ2h3PISdBiUIQp9MusDqWpkg9KjMNdFBh06nha9smjPrOBZ4Vp5aCI22T34kx+BQiW51iVpQwE45VNdWcLSN9Ls4qTEK2zrU1udoVJcI/DQpdwAVtOh6bVO5SwZ4Xp3Fk4fRWuCkUoRKk0oLMmkb2y6wtryKlJrCQB7fU1hK4rWbaDeNFBLLEUjTSQav1UbaFlqECCeN3wnxdRfH6+Cm0mRSNlK4OPkileUVstkiU9u2oLVmfmb5x/7AP+D8+8+bPw/uN5PU+1TRJVLv40smN3dq3pQ79FySJfX7E20qxrziY3DeHcmlT7h/WRs2iwsv/r9mseGzz/vxRAaYie7CpCmXmnD/IRQ89npj9v6HV8K/Pd9ER1/6SXj0rVGlnaRc7cOFMxYak1iQlXRCn9lGQCGfwY8nsZGQcAFXSNJovCj9SWEa54XCpPZ00Sy2K4wMD+OkzK1BRRP+vIS4oGwGM3lvBOa10upHLXwEgdJ42vSJcCV42kz0QZsqU1EVqLQEW2gy0U0oiCbmJtohTJQAY6LWGGN2qKEbQBAYf4kfV27S/RK6RH6R2JgthI4qaunIiE1UOapfEUujer034jK9ltQ4MqpehYj+jAzjgk2lcoV5gECZql0iimI4kbiLhojxixgzvI58MSr00aHCkTZ+EBAKSbfRJZUS2FgsrMwzP19l575JWs0WI+N5Ko0G69Uq3VYDbTnMLh6lVMyzvj7P2NROqqsN0D6WgqXZRQaHc6zMzzC/1GZoeJqpyVH+/+z9d7xl51nejX+fttba7dTpo25JtizLvSDjim3iuMQ4DhAIoeNASPImkJCXkJeEACEJgSRAQiCU8CMQDMHBuIApRu7Gtiw32VbX9Dlz+m5rraf+/nj2mSLNSCMjF8lzzed8zpy9117rWWuvct/PfV/XdSrUbKwew01hcHA368fWSCSKucRoe4Nu2s3EtsTNbUZTx/DY3Vxx+TUI07DUaMbbDqElkTFl0ef+u0+ydeIIzg6ZbE5YWppHFgbbRiZrq3TmK1JngbKj8W1DO9rA2TGTyYjlvZezeeoUk6bmxNFtmskayStinNCoDoP53ehCs7G+Tr+qaKzF0CWFKUqVLO9ZoGksyUakKVDRUcztJrptlJoSW0vrWrqdPrLs0KvAGMl4NMlGlCHhfUN/YZ6q6FFWhigSo40N5hcGtLXFxURRzoGIBD+lNAWD+QFlAbad4r1keXf/877gv+R41R/l34ubOVg9zTK6EGbJRX+ciZ83fzAnKM/5yBd4oJdwBrPvYDDK31l3ml2T/8EvZDWYS7iELyaigk8+DW6/MasOndg/83MR2QvjCfdknsZp34s065HYkYw5C1fdD299LZey5S8WUjbiGw0yz27vSjbrW9zMFW4dMrn/Eh5XuOhEo06JQoBQmhCPsB0vwypJJOFEQqREcIHoPVVV8We3vJ9PvOMvKXXJ3FwH06nQ3S57Dx5g943z7NpzYBYkn1FMyq0/AjPjILQk+gr0TO82twGBIgfZYVaNEECQQJETBpdENsnz+fXuTmsWzFqocuXAprx8hJkj96zlSWRFKSMTKeb3SZwmrOdaQjpdrcgEcTHzCckqTwJ27DCAnFj4mYytgFkFQ2DkmRucIKEE+NPGggkh5GmieJplOmm2nJodC2bHcKfZTMz+H2atVU3r2NxcZTiccPDyKwhKMd7exofI5okhc3PzDNtEHSPbkynCBeYHfabjFQrTZTxaJQSNa1psUbHZbNJZPICbDpnffRUHy2WKzl2kaWB5YR+9csR44zDDqcB6QbW4j42tY1S1pZzXtNbSZR5jJFpGJhsjwmSVTjVPzRYrRz/N3ituoCz6jJjgGtC6ZXO9pR2PSW0kxkCKntH2FN3roVOg39O0oyF2NGXLaBb27sVaye7d+6mH21RXXs0mmoXBHPff/kF0Z4levwPCE0dTJqMhGMNVN96I2zjGZNJgegukMGG4vkk7cXTnF7KQwXSL0La44QpKAjFgqkSIga3NNeaWl5GFpigLVNvirKcsJUJIFALbZhd0KRSmNKRoCbHO/6cmJg0u4rUHIbB1Q9ktcMEi0uPgoXjwWJaRfPurH3q5ss3EwB/6aThwLLdN9Mdn5NYu4fPAQx04ce4yKmQOxovfnd16l9dzsnHgeH7vcXAqXsJjDbNzU8yU54yD3/v6/FqU+Z7xxDvyw/rI5VwzDDyNlfwsRBMQ1Bgsko9vzuHu/itK435F4gH3kB0xiLQz9Trj0cEZJ3UR4dt+I5si3nVdJnZ/45tyRePKQ2et6ws99kv4YuOiE40j3lEpSRJwcuS4fl6hmXk2kGftVaGxWnKqmXL7PR/gw7e+CzdtEUKjZ34Dlx+8gm/9f76LA1fLWdtVbvEJgEacbvuZkhjPFJj0TO1JSwFxprqUwKdccUiknADMOB4xidOKS23IbUMTnU38EswI55yeTJ0Zb6NErpBA5mWImTt4PIsLsfMZcTqc37nkBG5mLKhnVQkpckuTYZbgzPZvp+lJiJxchdn+SCFOHwtmy525qWaSu3Oepm5QUqKNJoREt1fkJCOBcw4tFUpLBILN4QRrPWvbE6qiYnuc+Q4LCwu0Tc3xE/cw2tjAjVc4sGuJzROH6XZ7pOhpG0l/d0VUHVxboyrB5tY62jYUnQVMaLDHjjK/MMBZQ93WrIxH9HQH2Z+n0J69A1g5tEK/8IxPrc8S1cB4axvrDd0UMEWiGPQ4fmiDTtFjNF5lc3WFU2uRnkrMzc9hqgHTU0exvsUUc0ilSMHS3bPAaHOMIjIajtBKs3zZHkxH0joLJlG3kc1TI4TqY5vjLC33eMrNz2Y0HDM8tYWsCja3til6mmOHVhlvnECmiJ222HaDFCRFucB0vIZoBFVZEaVEVAXeW3S3Q3Ke/sIyvXlL8pHVEyss7pqnN+hT9aAZNtCbpzQFTT2hND2sKnDtGGMiUipkM6vZRYm1EzqlQmDozJe0kzEhOkxhCP5xQAa//EgOXt/xqtnD6TxQHl77h7zwR3+fZ0xezq9e9rNMikt8gM8fs4f/rrXstHvNvWfeuv3GrACzPQ/HDuaZ3ud+OBMwv+Zd+TNVcykIuIQvPf7xf4ZP3ZRN3ZKY8StmJ6Yt4WPPAhLsOQXf9hsMPrmHXe++7kGn7qSMfCoKnNc8SG71Eh4aO0Tu4VyWpX3Kp7Pvxb/6sSwS8dwPZxWvz96QEwtbZP+Ln/wR2Hsqfze/+l3Z0fubfxt+49vy+5fwuMRFJxrroxFdafDJUckuSmYJV0c6HRxHIEgJZcnGZIh3La6pEVoTg0cpyWh7m2qwzD3jBi0VWkIbwWhBz2i8gC6CHpk43egzM/M+5YRipvwKQuBiTiyUOnOjkGJWfkCcJmVLoBCzCoDaadA685nMb0ioWRuSIqtJ7UjJplndJYkd34qcCNidfY+z0u0sUdFnOXPnysmsRWyWfETOKGztjETK3ELGbJsipdO3PB8Ca6vrHD9ynMnGCsYU7L7sMhZ37aYqFQjBtJ4ymU7plh36gz6QqMdDtCmYDjdpnGczCbo3Pplefw7V01x11fX8yZt/h8r02Fv12Tq5wpprkabgCdddR91OWexWbNuGUitSb8CJE/eweuLTDAa7KLo9/OaIGDVrm5uI0KGNQ/YfvJwTJzcxcoErnr6H9UP3cuy+49jag0/09/eRW5ayt4fWByq9QDEYM17fxFnNldc/ken0YxjRY7i1jtQbxGZKEyTFng79/i62Tp5ist0SRAuyh9AFEYvQc0Q8q8cPsbBnP/XaGmVPc+Lwp2lG2wxln2tuejqNP4XsQlOv01ucZ+3QEYTdQHR3Md5YAyWRMuB9QzVYpp1ImtEYrQyFlphej3YYET6b+nVNQUoFRkucbdk4tUkSBcZU9PqC8WQEvR7dbp+t9U2q3oAUAs0E5ucE3W6JpGBaW5KztNEQo0MP5qi6PbxrMUbSNo93edvZdfmsWxn86M/wNevfzYsOvZL3Xvln3Lbv/ZekVB8RZnwKFXI/9D/4hdx29tRPnlFwAfA6BwNru+AzT85KPEsbebYYLsVel/Dlgzf8fiYO/8wPwntedAF+hsiB7y9+H9Xgs2RZmAcsszg7v//Ob+XAeDiXTSjrTuYM3Hf1jCCezpqxv0QYR4Z8zN74y+fei9/51zK/Ymkjc/H649y+dtnR/PvF784Gi5C/ihe9J7/2ttfkpOR/f1NWk7qExx0u3kfDdFjf3uLOu+7mpc95Jo5MABczIrKdBd6ShIsBjyT6hJByxlHwIA1F1WGSFKshUpgCpSQxRrwLxEmNMYay0EiZvSSkzA7ZnOYqcKate2ZKtxPTCyEwMucYcZYKxDjz9Jixqo0UKBImN0TNEo7MZ/C5Oel0pSWQncQzOfuMVGxOM3KitZOgSJkVp3zMLU82zMZwmqshULPWrDhrfwJO3/vkrLqx41WiEnhrqZsGFxI+eY7edzd333kf+Jr9B/axceIeXDtGczmduXlOrh1n49Q2wkcWF3cjtUIrz7GTx7nrM3eyeeQI1990E9vDMQlBUXWQqqCz9zLarVOsn9xga2uLelwTkqLfW8Z0+hgzYWti2d5YRSfD9uYmg6UFtk4dYmmXRRiN6WhSO6bbCzT1lKNHDuN8wcb6Jtc//alM+w29+W22N45R9SqCneCCY31zk87uOdZPbNOOpjRNjYiB4fYpCgXjrTWkjBy44Sa2Tt7HcOhJ25ZJs4ULkXbcMhhU6I5ATBIydtk6dpSD1+6lP7eX4dqI/lyXlZVTFCe3cPWEzmVXMtwcMVpb58Dle9hedYy3PYPFPUzXVpisb2FbiykFSmmKbg83dvSXlgje46abCF0ig2KwvJvRqZNZmtk3OB+IlaHb6TOtGqbTIWVXUZUVIjrq4ZTkwLYOKcYYI+h0OlgXCDYR241Mzi+7EC1ReFKa4lzK10oCaf5qF/2XDZ54R55Vv/eas0ruM1xzL/zEv+QJuwpe+ZffSBEG/KO//En+9Uu+h0Pzd14KfB8Ws+rFUz4NP/gzOcm4/EieiTzfsTM+//QOw5WHv+ijvYRLuGj82L+C5/1lnh3/jl/nwjcDAV5Tbs4hOI+pm9c5KL71efCnr8gJxp5TmWT+K9+dk5jf+LYcMP+d38qv3X3dF3LPvsyQchXz8iP5PtKd5pe/6kO5IiofMOHz4nfnJPDoZWfaWwsH3/Mr51+99lk97Bm3wff+96xidwmPS1x0orFYavbu24XyG9C2FN0uRgrsbOa/OyNVT2JiXHt80+K9QwqJKXKLT4oRYwp27VuimOtkXsGsW6QqDZSGGCLee4xMGKMR2XFvlg7MSNTMAnyZFZh2VJ92qg7nGv7l34LcZrUDQeY4aHbUmvJ7blalyMuI02TvyJmqzU4FIs42IE83OeW/QwJIxLCTE+V0Jkp5moexw+GQyBkXI4/Z+cD21pCt1U3G403WTxwnIhjML7I1Os7WybvZd8WTGNdTjp0Y0z2xxWVXXMNkMsY3jvFwjfWTx/nMyDMZjbnq2htYWz3O1qljTLenrK+t8blP3s7uPcvs2befJBKTlTWC0Bz93G00WyOSKigrWD11nMJUtNMpm5sjut2Kja1V+nOS7dXDKF0QgmP3gcsY9HYzKlZJUWLrhtZBmg6ZbK9y90cSQVlcEpSDim5/N01bU1Zz9Od7VINFxhtDjIxMx0P27ruc1ZUaXfaYTk6guh1GG0OE6rC8q8P6iRPIsICzEgrBZHuCESXKSFwdoV7h1PEOvQWBVjVrJwNbJ1aITU1vbonQTNloPUlItk/VFIM92I1VRHAUgyUQCXtqm7ZRRCRpOsp9dE3CKENRdEnJYq3FzvgiIcwUxlIgWoUPgYVdS2xvjxmuTghzYEpJM26oASk9LiQ6nTlCDIRmSkRRlJrgE0IJpNaUpkRLQ4otbZs9NaIoLvay/fLGa96Wk43/8ENnHvRNlYPhH/oPlC/6EC/4xD9HxZIEPHXlq3njrf+Sn37+D7DVWftSj/7LFLOb3tJGDsL+n/+SZxThUnJ2CY8PfPv/hF9+Y75XPJSHjohw9X0U/hAcnn/w+2u7Mpk8zQTqyzbfg37yR+BnfyDzkK48lFt6fudvZ/fxrxikvP//89tzUtcfn1u9ON+9pGxzNfRr/+TiNiGA1741Kwheujc9rnHRiYZTgom1qHKee1aPo+f7WBdJZUEkB/d16xhtjwlJMF1vELPWoJQg+QhGY0zBoFCYlBuEbJvJrsZIkhBELVHI0xWEAFmyltxKlNuYcqivZ1K2O0lDmr3nT18QMzUmKVBCUKgdn4kz2ElaIjtJQ5az9eysN85SgZxQnK2TI2eyuWdjlhdlN+7Tk7Ri5mie35ApK3Dt8Eh26PApwcmTJ7njk7dRjy1zSyWj8RYxQcSycnwF2ziayYjt1ZpYKIqyDyIbuqVZX9fhe+/FtyXRjrjXtUxHE2LyRNuwcfI44+E2Wt5AaUpq56hHp1hdaXGNxbuGYB1S92gmNZQRVTrG2yewow6j7RHW9onRsbynT3KO7bU1piNL1JKTn7sX0y0IocGPWoSznDr2OTqDHv1en+EoEOQQVfXw7RZt3UW3UBUlwzqiiKiFBUzX02zW6E4flTzjlSN0lnYRUkArhfebaNkQ2imdpSVcdMwtLLPeHCfqeUxhkFoxGkV843HTLRZuvIH5XQO6S/MoVbC5cpS1k+tUmz1Ss47REN2E6WhCqRUBRaEjNilShLIsMSpQW4HWBp0KrG1ISUPy+BgxUqFVovYOU5XZZyMmhuvbdOZ6aCNppxP6C3N47xBSorXGxRofIkoailklKl87FW0bQCpModBSkKLlMY98QWcFkv/6/blt4dQeOLkPnv5xWNykY5f4mnu/DpWKWSVR8oLDr+RXn/Hvzk00xNllzkvghs/Cz/9DeOF7c2vIpcNyCY8n/LOfzn39Vx7K7TdVkx2lT+4jz96FfA38/f+G+BtvofPNPwaHn3GeFYksLjFeyIn5f/on8Omn5IrJtJerF3dfB7e89Iu8g18GGIzyPeTlf3bx948oc0vmI/nMA6sil/C4xEUnGo0LTKznxOY2a0dP4BigvENXXabThvHmNkIo6nqKjJ76xDqLS3NM2ho7cbnFKWXuhHWRrbV1pNAsz/UxpcKTTrtsy1mCIMhGe3HWygRkJadZhWOHx8COSpM4c35rMWuhOn0ez9zJRTpN5s7O3zvrmlUjZmtQ7CQleTvxrHHEWfI0G06uqMzkqnb8LIg5iQgh+2pEEikKpJIzNaozfIwdlQbrHPfccy8nTp5ifjCgbT3DNmKbCcwSGF/MMdw6BRTEJCGNueOzd9CbG1D2B/SXl1CF4uR9R6jKEoqSZrxFiiClpq4bqrkCU5ZM2pbPffyTHL/3BPWwBpEoyhLXjBlaR6+3yL4Dl7G4b5HttXW2Tg0JMmKnDaoyNM2EycgyaBNQIwtLM5lmLkFRsrR/mem4wOxZZuvIcfbvXqZJiTIFms0NQgi48ZATx+5jYXEJRI/Gwtaho+y+8hraaaAdOVCeJjomQ0vVG4BOTDdHaKNomxY9ntCdLxlujRBBILRgtLnNniuuoK23mI6myKpPOx4T+30+/ZHbmB8sIGVisnU/206DUITgmIzrfB4IA0S8BWXyt2obi+kYcBaXDFJ4+p0SVxnG22O0kNT1FCMKpJAQIkWvpKoKfKipJw2DhQ6DpZJ6MkGWcwxHNf25kqLbh7pm2jg6nRKcIMmCXq+Dbx0hWhIGaQLJdx7BJf5lDgFULVSr+aF/4+2zlyWvvuub2TO5PF+hOwSpM78yXvSePMP5pm/Mkolf6VF1YTN5+wXvu5RkXMLjEvrEXvjZf4z6qg+g/t0/R/zjn6b48DPhfS+g/OjTUZcdQX3971KFiPyj59I9uXzhlR04Bj/2fbnq96vflRWQgMfzxIUmUBKYcIHKuLHw4/9f5sE8kkOgAvzwTz0qY7yExxcuOtH4xX/yk0zqCdPG4cbbSJF5A01T00waonfEFIkpEF2gs9yh7BjarRaiQBUKIRPee+68406a4HjCE56ANLkmoFIk+ogy+nSbkuRsZae8XJq5i/sdfoYQp4nhYqbMFNlhbGdehyAnF2HHaE/tkLrzVXSGMXEmodlJAc4kAxBiJEZyW4sAPzPUy0pWEFI8nSBBQop0utUrBY+MHnQ2KDtTCUnZPDBBMx1TFAllFKPJiNYWNNMaIwUhClTZod66H5ug2y1YWdlkz659DJ9wFXuuuILRcIMTh47irEEXhvHWNp1+j3pUo7VCGos0fQrTJcUJ25tjjt7/aZrRGN/UFJ0KIcGjKIh0F7p0F+couj32XXWA4XRMsAnrW/rC0F/YTWOnTCZTDl5/NZurJ4hll8GyIE4aevPz2BSRPrCwe5kTh0/S7+X9ba0DPLrsMN3eZLw5S6Z8pBk7Tt11O15KYvAs7lpia/UkrQ0Yo1CqQ4jgRg0hOMabW9TDgO6WQMROVzFVF5ECMXrWT65Sj4acOnKEdlIz3tzk1PQOtO6SZMB5R1mVtK0nOo+ICVkJEBJVFBit8K5l4hqG0YJPGBERShGCQMlEVfbwoaUsKmL0CAVlv6DQJW3TsO/gATY3t6lHU1AVaIlvpti2RYkunbKPMZIQHEUp8zZMQkSLUoKi7ODaiCkj0jw+H4CnH2oJdDRcv/40em4OLy/wvBNkdaSf/4e5DesnfySX7s/bTvFYOWYPmOHr1Plm1VRnvXihfUl5ZvYf/nxOOB4ru3wJl/AI8AY+m+8RH5xH/Y1/hxAR1ZSZlhQU4lPXwTt+5MJqdjPIokW98ZfglX+c+QJru6Dp5OTjurvg3S/mwf0PO3isXFzn3k8EsJcJ17LBu7nq/B85eAz+7m8+csO8x8ohuYQvOi460fj4x/4cqTS6zG4XSQhMUVCUkkFX0OkuUVQd2pSwPrB5/Djbaw0d06G3t4NrW8pBgdCeew/dgzGG7mBAb9DBdDvU05bQTujMLSLNGXeNhGC0dj9zy1fmhiqR25NihCTOeFnElHuR8jzELEERZxSddlqUFLl1KZFdxsVpJsYZ7Gix7JDARS6jsH5khc1j2xy86QqSlDStJYWIFJEYQiYC+0CIMvtqhIbkPUFopC4YLAzQKp52BRczVaoIhOA4dvgIk+0JMU5wXtK0nvUTpxj0OswvLGJ0y3S0RjtOLO1ZYjwacvmV11CVFVoptFK44FG0tE1NcFPWTp4geQihpl/tJnpN23hOHt9g49Qaw9Uhbd3Qjqd476mCAGdJOvMdpuMG0+ti5vZy7Y0ln3j3+4g+EdMSJw6fYPHAfhITJuubNKMahSe6OXSvz8mjp6g3Ntlc26TqG7Y2x+AlRZXQBfhWEhzU0xalPYUGLTU+tvjaIpF4F9g6tkpUkVBbRsnjNzZnVSnQalZ58A1+OEFpQ/Sepg6snbC0dcwVsBgRwTFcP0lKihgtTe0ouh2KQpG8JbUtUkY6g3JmvgiKQGsTISq0MAgRSJmMgS4M0UeS87RNnUULgsfaFlOYbJiIQmhDYSTL5T6OjO5hvN6gO4o9e3ezdeokk8kE1waEUCQRYXuCNCW67JOSJwkFSaK0oKk9Tfs4kLd9GNx85BW89P6/QRBnP79m0wEPfN5Xbe7zff4H4O5rswHXTmD+yadm6VaArYXZ61+KJ2ICEdGLI56xZ44r9kfecdcatd8pjaYzsxvP+Uj2B9Ae/to7c+L07hfn1pB3vxjaEjaWcqsC5P7p/jiTv1/xpznZuPTQf2zigZ0kl77HB6G3w6JUAWwOYWLhCOWDW0q3rz/M0ievoxg9ePJBFTX6pX+eCcuJXB1989/MkxZ/75eyVOtfvBQmZwxSX8Qh3suVD/qavnhIaAXPfqKgU8KxuybMj7co8SxT08OiSOxjhCZR4dnDBEgsU+OR3MMib+FJF96EjGc8MC7hEh4FXHSi8YM//x+JKFY3T9COpkwsXHfdE1lcmmNYjzFeMg2ev3jbn1MNCrYnf0SctNSNpfQ9toaOKgQKOWL92BEWl5eYTCYMxy1p3DCabLG2tsbuZcvey/cjdTaq885yfOUEuthNr98/kxSIbFF3uuYhIilKENkFdObxRxIJJXJr1E7CkCIkKWZu28xI6bPqhzhToYgx0jaWw3fcS2pb3vmLv8rJ+45z8yteRu+KZTY2T+FbwcGrr6S3W/CJ991KaBwpap72whdx9+F7mR4+QkiepaVdvPybvwmEYmF5jiTyNieTmnpsqesJxw4f5uS9d7I1rJmsDxlPW5rhJt3eHHZUU833KYoO237E5sk1FJH53XtYPbXCaHuTunGs3H8fq4eP46YNbdsgrCBFj9KC6DeZjqZMRzX6+EnGwxFtXeO9J4lIDB5vx6SkqOuGw5/7DO10jOkt0A5P4WxgNPQo4QnuKFJ3sNur6P4S25uHqDdHiBTZLtdJKYJ3TCYtykDTKHzrIDmIDkHEWZdJ1kKDMrS2JiZHISIiCYQRyBhp7BhdFMRocXYW5BuN1gnfBkJKDOY0uuzT1o7gPG4yxUhJIhCjoNvvsL05YjDfQ+KpBvO0kyFVx0AK1COL1hFTVoQQKIqKGKZYK9A6SxHLyuCsoDASAgSXSELgWpuDSKUIAkJMqBCwIVEoSYgaGwRKeXbtX2D1xBb4yGR7k87cgNH2iKJXIIXAmA4pRJqmYeXYMRYX+yzt3wtC4hrBoATxFfAQ6Lg+PTt/uurIrDL5nivfxnp35cEfEMDutez38FUfOvP6uJ9bqpKAd31N1nT/3JNmQfrFRnGJB0nqPlAl63zLV01uyVARrr4XXvnHPPHZq/z0x38ZXSb6V/0af3DVb7N4MvJVH9vPdfcs81tf/2nuffr90K3PXeVr35rlZzeWMp/lT1+REw7IfdE33p63ubCVt3sJX/443X6bz6PlZoHlZoEbtq7Ey8Bdc0dY62yyUQ5n/L4v4Vi/jPDmXbvga/4iG77NvBd81eK7Dz7vi1GP193863CeROMcCLJi0tX35YSjN4Hff0P2eXj7a4AseX81W9zKgQu3HT1ovfFh7hUXg3wXrHC8Tt/Oqb/W58df9QSMknzgXZ7qdz/Ga7mDfYzZpMMhFngaJ0+b+X6cvexmykFGtCiOiT6/vvsgTEd5EiOJPKmx51T+/ZJbckX0Ei7hUcLFO4PTw8iShX0LDLvHGK6foplOWQ0CdEVnUHH/nR9nbkFz+ZOu4dZ3WYieGAKTaU30lsQ8xaBkq92imBScWllFRsvc7j0EA0cOHUXGHkt792BErl588NaPcc97P8iLXzLg2mffiEDgrGO0uUGhK+aW5hBSINIscUhgvUVEUIUmhsBo2qCVouqWmVMxI2+ktMO5iFmCVwistRy65zCHP3U/J+4/wonjRxltnaLoaI4fvx07afnIre+md2oPTV2TYkQbz/5d+7n/8L0QFQrJwZXjbG6fpBmvgtaEVce73vpOlnft4crrr6U33+X++w5z16fuZLS5TlNb6tEWzWSNGGE8HOekJwRCTDR3TyjLDuPhZlbzqiqkThz63D3oqmJ5zzwrK1u48alZAmFpJwGhanQhwUM9HcK0xFqLMiXJ5yA8J1yKEBOi7EBd46ZThm1iMh4jtcG7gBABhCAERzIBrQuaMMHUHll0cK5FxYhv25y0JYvzMkvEVrlK1Y4mSBkpioJUaEyhSRicDRACtvWkmKWPq+4cdpxd153NpnYpJZQSCBGJPtsdKlKWnXWTmXpXCyISQkthDMmAVgIhPboUlMbQtg5lBK6tAZ1b6ZIkxoCPCeE8MUaSTyQKoosgA6pQuUXQRYRtSLrEep+rSTbhosrtfEKAVKhKo4MlBk8SCaN6LC0HtremjIeW/pJExISICpcc2ji0CAgh6MwVWOfYPrVF2e9jmxphFEk+TlSnLoDlei9/485vIaHOeV2kyEcPvI/t8jxSlacXesDfg3H+SWSJyhe+F973gqyTf3LfxQ1IxqyksmNwt7ob3vK6M4H/PU/ILU43fDbPsl5zb15+eT0rtmgPxtFRmm94/3+mpyuE13z/Pf+UV5x6Ps//t+/mwJ0e4zTPus/wfb94lBPd8+xLaWH/yezUff2dD7/vl/DFQwKdFOJh2nV0Utyw8QQ6IV/DT9q6mpcefR4SeOL2lVy7fQVFkiTAicgdC/fzl7tv5w+u+TOGpub2pbuY6q9s48StW1+Q23tkfNjjIL2++GO1bwVe9Udn/jYut2bKAFHy7dzGj3ELAL/B04GUeWU3fzAn+W997RkX7J02xu/49Wws+L4XwN/6P5k79aevgLe/mge1ZS2v50mCjSWoOywvHeM7wyc46Gr2ijGrT7uLp7DKH70hQvwFZKjQS12eL45yIGUj00/rRf6LeB6/6d5Kj+y39O6njHj6uufgCSgJ7Oqtot72ahiYPLZf+W746vdnkr0KmQj+OJzMEklQRP2gljoTdX79AVBJMXDd817TS+0cOqkHvT7fDuifh0NZ+ZLdzeKDTsUiGKJIePnQ3lhjXbNRbT/kMglYr7Zo1UMniV4ENsrhQy4DUOuWRrUPuxzVwy9y0YnGZO04temwa34fC/PLSF1x+O6j9Jbncc6xWfTBe5b2HiDFHt6UeL8FEXyTW1KidYw2p8yNataHx1lcvpzjJzY4eXSb7sFFjt13H4VNCAl1Hdi9bzf33HMvw61TrB0/zBX1E0EJ7rvnLg7fcQdXXXE1iWuo+h3qpsa2EduOGW4OaTYnPOmZT+b4qRMcu/cYy4M+lz/hGoQG23i6gy6HDx2nqjo439CpughluP/EId71B29D2cTGxiraVAgdmR8E1nYX1FPP0E9ge4TU2dfg0KFjnJrmwFekhCfwqdtuRymLEBqQtL7lrs/cyZ3pPj76gVupuoaNrU3qSQ0hIYjEWBN9i2sakoeoM0cBFwleYesJzrXYNkAKJKE5dt8hikJx8lifzfUNFucHxKiwtcNaj1IeMJhC4z1IGUhR0rYOEQNSeFLMkrxaKmzrsbXHOoGaVYR8W2MKjRAKUoNAElwLKEIEmVoSmZ+StEL4JjvBi4hJkrJjEFohpCDFAik0MUa0lmiZcL6lqgy+9SB1JvgTcXaEUIFgPVW3wLUB7wOq06XSiejBti3dfo+2saA1ySfKQqMLRUoBbSLRJ0xV0R306XX7uOhIsYYQcI1D97ogLCJFiB2MTCQc/f4cSsHayhqduQG6VNimRemEEALXgJGWblfhXKJxASkVVVnQWoezAbe+idKKotLEJJA6kERJb8GwtbLBdOSQyuC8p9MvSVEyrgNKCAaDOdqmYWNzk6Jp2L1vF0TH9nBysZftYxId1+dJa88ALkTO+Dywo3J11aGsVvONbzrTenQxMO5Me1MU8H2/mB9Ym4twxxPzDOiTP5Mf1DLm5OIBYzfNEjeeeg4yGaIAQcGThi+E5wwwd7wNkSKvesd1/ODPfDX/5kf/guH8BW7yj9Ug8zz9Jvkhf3HGMH3X4bkrTyOJyF/u/WQOuM9CEIHmYR6ywIXb7h8CZ4/TRM1zV25i4Lqn369CwevvfTlL7dxDrycpbtq4liqUQEIlgUryzJDEGRJyh8Rz1p7Is9eeyBs/93VYGXj7le/hd6/5Mz6w7zZOdtcevD9fCbjiyBdvWz/8U7maeN/VHHzTXg7cIuhc/2loF3Ib5v99fZ5M2FzMExlHL4PFzfyZ170lV1ze/DcRK3tJ/+w/ZmPAl9wCe2dV2cVNeMer4K//EXz/f80VyU88DX72Bzj4ot/jikNrvO01d/LP//1X88a3/A5L4z38x3f+HkXI1cybniBYvrxLOpxPg+GLP8Rnvu+38N/2Apjk83XzDe9geOsBeNv1eZsiwfwWXL+R5cW/8U35nlU8xlpyL/LalUlSRsPX3/tyfvCT3/KgxKETSrr+wdGySpKu65z38iqiOW8CIpNEPsQF+cA2/YtFusidDSI+7LJJJKx8+O+6VZZWXcQ58S0Pv8hFJxqH7r6PKAeIKzvM75qjHp7krjs+wUAvMmrH0DjKXolWku7yBnayTfQRIRXBOcpBQQyWZjpha20bvMB98tOIUiOiZG61z8o9d5K2Rtxz5+dIQdBdPsDW5lF0iBw/scL8Z+7HqZb3/vmfwnDKZMvy0Q99hrI0VJ2SpODwPbdz8tg2l+3Zw2g04eiJY6ydOEGpS4oPfJqyaxhvj+gP+hy+/27mB0s431J0Bxw7egKdEi5OsU2LjA6lNd633P2Z42xubRGbyNaaZ7xVs/eqAwip2d7aZGvcEsMZBaytjTU6HUNZZKleISXj8YhcgI2obbAuZLK4T7kdJmbvjUgiRI+IBucCSuaWsDQjpKSUiCGRZKBtJ1ivqJKllBZbjwkh5hn1lIghn/hSaaIoqDolptSUMo81hIhQEmyPQgnaENCVoOyVBAu6FMQkKMysWT7lv6MTFFWepU+touwrvA0oHQi+QEqBKQfEkFuPhHbEkEnNKcW8biPR2mRyvRII2aOrNaFtCCkSW49SAmUMwUdMafDR48YtulMCHlVonM8+FoXRxCLLBhitmAy3cUkgpEYrwfziHEIkQmtJKWFKQ+NbmvGUqtDZWTx4pFEooXGNpY2B/vwAISVu6qiqHu1kSJISgcpu71IjhM+GjERCUDhbo3WNVhpZanzjs6t9FGhjKJQkLS3gnCd4SzttaWtH1SnBCNq2pqgrpFAs7lpge3OTrfV1ym6H+Dg3Bo8i0ugJ87n38dEPpATZnO7zhUxnHsr7VvLPwyFBx/eQs1kwMXstCcXo+quYXH2Q/p2H0V7wd3/z6fzad97KZ+ZXP/8xfinxgOfcwHXp+AqVJC848Uz21lkFSADPXL2BZ63e+OAPnQdVNFw52g/AfYPj2HNmAQVH+yd55+UfIIjzz8ZG4IP7buNY/8z3JZPk5pNP5+Bk7wXHIICnrl/H81aeRk4OJFeN91OGcxOkR9KMlxMKeWYD52ztrP/PTv8CQREM33Dvy/ib976Mjy/fyU8865d55+UfODOD+ZWWcPwVkVQk6XDhBQRw8Hj+AVh5MdzyYvZ946/B3/lxuOUlOcmQs+rFW16XJy9UOFfx7Q2/z8JzP8Fw3+HM/3zqJ3MVYQf/8icyx2pnLC+5BV70Ho5tlrzps7uoGsMdT1wnqMRNw5dzpXvu6YC1N1Csf+/r6P/mHzL47L3QqRne/DEmc89mfpZo7DnV44LX1w7H7ZwDc4HlLvT6+T4byNKdFzonAzDNlYFr4kFA0OiWkZnSKHt6Jn2nqrDYDpi3A5baOfZNl9lXL7PUzrG7XnzY4H2xnePAdBdXjPfRd93PO9j/UuJix3y+KsuDkLioyZ2+7z7sMheLi0407vzEnQgluPdTt6OlJKQp3g0Zh2MI0UUoQb0xxRRdmhDRXkF/jnYyot+fAy2IIoL0DNfW0CiEMDSqQrQta0ctzWiT+7cmdKqSqLqIU5uU2iE6BcdPrHDkD99OO9lmNFoh1IGVE6tsD8doYVjeewAbpmyvHsHIkhPBsXHLGOstyTsimpROoAQ41yJlYDIasqHW6FQKoStWV06ShiPKXoUwBt1VlAYiU9Y21wiNx2iNaz3OjthaW6O/uAslBUJYUvLEcKa9J1LgbEL4iNIpG7t5iyQiFcgkUARQAakEKXkQAa0llog2mZFclLkaIHWCWuKDREqoepoUPOBQwmG6iuQaQvQUhaCtM89ESEF3DqquY7BbkQi4ukVi8D4QvGDrlEdVJb3okDIH0JQCo9WMgC+IIeG8ItmA0proE0qVJJXwzhOTR0RwjafT6+Kmgd68oQ0W5SRSBvSMTK0rRWE0ENFKED1UxpCSQpYVwjb4EDCmyslFjBhpmJsvGA2nxBAQwiPFzHVFBNAK4SxFVSEImEKhCkWv10eZasYPiTOneZBSIY1GRE/rPGWlKLolaEUhJdPRCF0UIDXJR0JwBK9IEnSlsikkgXZqUUjK0mDbGi89QmUiuzIF0Qek1oSQ/TdCtBitUVqhTYkPJbYeMxnX+OmUpf0LWGEYrq+hTIeqX9AfzGNKgXeO4mHKrI91rPQP82vP+I/8i/f+AonHgQ16ygHtN3/qH3L58Lozz+vZw9suL3Hs9S/jql//A8zaNu/8a3dxas/komfszsEX8hn6EOMxUXNwshed8rX11PXrefbKTUiReN7KTVy7fQUCWG7nKM95yO3o/T0yPHF42YNee8rWlbzy6Fc95PA3yyH1WVUPkQRLdkAVHq4d8TzjPKv68MjxSD93JukwKfHs9ev5X+/6Sd52xXt5777b+N3r/piNcnvGV/w8h/QVBt9tsINHVh0WiJwPXH9n/hGn38itjeeDTGxecde5y56NhQe0xAhARQ4em+Oq+xdxJnDbM05y3cqTeOPHfvTcoFMI7NI8R9/wtVz3X34TgCjTOd1BOinUWQGoQHDleD9ia4BKkuVmngU7YM722FcvM2/7dCYlv3n/O/iUvBu+BjgCPB14J+xuFtl2Y+xuBy8HzuoW42hehlXgFSCeJrniHS9g+9ojbN1435l7yEeB34eFOOB/Xf3jXF0eJIiAlZ5W2dOVSZUUfdehDAU6KeRZ1b/HYsLwlYqLb52arFOoEqkmhHKKtYlSCYKsiZOALAtcaxGNp0zZ6K6e1sQUad0YokZEEP0yvxYCabhOxKCNoG6n+NaRRMQSSKJFlgNwCaNga3qC7WENdhvvPEIWqBRnLUeWerKBDxOCa1Ba4lxLrIdMJw0ygakqYmxobJadNWaK0YEULVJUuHacg9toEVGyd98SW9sjmvEWx+4/jBaKEAIpZfUq7wLj7W2EMlRVlwJHM5mitSISIGrGdgwuUVYKpRSFEoQYKSs5U8uSCJVAeNARhUJKSVsbmkwmQaSCwXyVOSWA0QKtQSlFWQiszex1JSRaBTwJLRzttKHTkxQdRVVJCiNQQtGO8qyJFJrJ2FKWJcF5pI7IJAg+YToFtm5JwWEGkrKEZpJmXh4RLSJIx3g7UBhB2VMQEwJJdI5CQ4yBqioJ3qFUQCSBtxIhs0OJKRTOOkylCbbGuQhqgLcWSSAJiVACkkIrQYqC6HNiMej3aK0nJYUPAi1AFYboPAoFQlBUgj17r6T2Y9pxpK1HJC+QMmFrRyKhhMSoTEZ30lF1C3TVoW0bYpKYwoCQ+CAIUTKY72PblrIqUMrQXTTU9ZToA7oQCJ8IQSCEJAVJENDpdbBNg9QFhVa5vSz1KQvw3YrWejqFQiWNc5pet0+9OcVGSdmvCM4y3LD0FrokpXCNxaiLaIp8DCOIwPHBIbaqVeab/TweIqcbV5/Diw69GpnMOUHArPDI+JorOf76r+W+1f8fP/G9t7A2nMKngHu4uN3vAs/i3Dt6AewDpXLwH1IEB5wAPp8W7BHwsQd/9q/Pv5Bvn389LzrxtFnAniijOSt4z/fMs4U8vhAQp7d1gfcTLLeDC7/5eW7xiw4hEAn6vuRv3/ty/tZ9L+Nb7n4VH9rzaX75yb/HnQuHSF+gY/y4wufJ0V5e7575/NlIZ2aUvQgPKk49cNmzkdvnzrTMzYlIVXpe95Yn8o9+7o+57N45nn/ft7B/fNWDA2whqC/by8prXsBVquWrN44Rnrcfmn0wX/H1e56KnozhBZ6YoCo0b3n/K0gfNzPegkGmmVZnShxzq/yTIz/D7Vv3wDzImwRPfs81fK5zP4NbevzQ4rdyR3uIP77tA6yV24TdAec9DIHbgNtn4/odkEPNM3/4e7jvZX/Ox99wH2yC3lY888ST6McuwzDhl06+mZ86+A/oyIpOIN+jLuFxhYtONKKzoAQJRfABIRraVhLkGCUlKRWUZYH1nma0SmglupKkxqFUJCGRVaKtWzq6QwgJ7x1lmRDGYMcW5xMCDyahlCb4zaxG1IisjNROkSIhU4AUUbGlN+ghIkhaQmwIjaVFUJaS8XiNWEcGCz2MaAmpQZn8cBfRYhAINQLbMKmnjNY26ZQF/TlFbLfoVZbaDXHDMapSeOuQhUEYkCHivaWZDFlYKpDKM1iUGKOy/u7swSqVRMpECA6ji5mLIAgREXImxysghjQjMjuqOTAdgXOR+b0aWTiCzSaBPa1I0SCEQupIjAopZ73jQmSDwljinaM/XzLYpbNvSIB8dwuzVh9wLmJd9vfYt383o+E2rW8oEKgiEp3DR0XXFOgONBOLLjRJaKpel3a6lVu8VIlMiUBu1RJa5OqNiDTThrKoUFpSdiS66BCsx1mLtw6tFVpXkHzm8YScKGa3eE1K0I4bhJRUVUFRlXk/RSSGWSJiBEqCkCXBW1zbMr/UZ3vc4GxEY7JPi9Y5MaUleI+3GqEEbdsiixLvGmJUmfiuEkWnYLxRIw10yx7NpKHqdBlvb+NTTX9uHik7FCbgQ0vyDm0UUiga2RKDpqkTRWdA7ndyBC+JIRCkJAVHv9vFtjVCF5SVZDwZ0zGa4B3B5va6QhvspKGslinLMrfoPc7xkQPv4j1XvoOv+9x3Eb4U8dxDHWLPIwrUq9DlBYf/OldsX3+B7oO8g3c+pcuPrH6AO/9wA7bZic8vHu99wN8FyMsEb9j1tTy5cw3/9vgvY1ufZyc/n1MoU6cehPvLo1z1hH3s7S5knpM4X0LxZZAsfhkM4VHDWR2FOsHNp27kq07dyOvufwn/8Wm/wW9f/3aGZnJ62Ut49NCdGoqoWWzn2CiHRBG5eeWpvP7+l/K8U08B4KMHP8yhp/86UxGpD72Ipf5J1NLd7P/c16GGlyOTYF+9jI65v2ipnaPjM+9ibv9t7H32f+f2XUvc+cZr+OG7/h6/9Jd/wNOLV+YJhfNBCI688CbesfK7/MN3fguHXiD5qDtJ3Llgb4T1J24znDo4ehlq6+O4Tc9Jt3ZOT/+K2+CO5hAfr++gO96DjRPKP4v8RufHeMs73s0f8X6e03sy/3jvN/Md9/9rdr1rkaN2hf+z+ecPHtMI0v+OhA3LCz/1bK7tHuH/bt2CJ3Arn2WgesypHp9p7uUd2+/n6xZeghCXTtbHIy460fAJvA9INMkrIBHimNT20UbhmhpVzTStkXQ7FcwZ1o+tonSFbS2x1nT6EWUEIkm0yA7ePlq0CAQ8WpW5pYVMuMU7fBzR2EC0FqElxuQ2I0kgOZerDFIgo8fbmugbev1EM62JTcBoT6FzMJwECJkgZV6EIJAI9Cuorh2QEphOQiiHFoKtkxO8DYQQUDpXIJJPpBgJrceJmm4pKOfFjGcBYtY+kEXpAshEtDWmF2fmHDs2f4oQQKuIbSOShLWehEKZACHPcoiYZlWHLLMqkCgZECqvT6qZclaSuQdcetCe5DSESIgKqfNyzGR7RZQok/B+StMAckKnl0gi4SYNQoE0XYILeJdQMmT1KiK61ESn6A0GuOBwtsGUPbS2CGPyc00URCKqII9dlOhKYeuGqjNPcBZT5iRMGoGWhnS6E0HifcjfsQwkkdDCY4o+1jVoqZGC3LYlFUIXDPoB20SsDzgJo+0W5RPJdPDCI4Sm0+3ifKRAENrMT4ltoKoMtW/xjSAwodMpkYWkbhrKXpGJ3zESfWAyHObzr4WyEDRTTww+c01MQYqRetRSFpLWQgwRGT1JaqSWIBo6A8N0nEBpAg5lCrpKElNN2wikNsx3K+o6ywG3rWPX/C6MFIyGNbZ9fJPBEZmnER8qmr9QoPxIib4JqM+z7DZw13mWD8DHybP7F/lMXJb7eMPu7yJJdUETsZhafvHYj/GJjQ/lEseF2iweCjuHa7YvqpY849iT+CZexZVmDyfW1viT4fu53x8/E1z8FZ/rGsVL5p7D9dUVecOXAoUvOnaCM0HimvFe/vMH/imvOfRifuJZv8RH9txOIFxKNi4Ws8tCJolOapYIZNz6/BV+5v/9MJ942grf+5m/xQ9//Dt452Uf5L37b+NnP/gDDOyZLOD5w4Ow7zYwU9InvpX4xDfDZBm/ejXhc685Z5NtstjkGIYpTWw5efhK7vz0d7Jla9bXx6y7EfUWXHH99Q/ZLiRlyZ+Ob+ffnfh5AALhQbc1ERU33PM3+Wzx+6TzKEtJJP/mwPey5reYtAN2r97Ihv4wgxt6/IvF78A1nu0wRiF56eDZgOCW4UcvOKYoArYYcVN8Fj9w2RW8cPAM/nj7g3xkejvrfputMOKwPcm8GvC6hZdcOk0fp7joRKOSCUFAYIGI7hnGm8fpij4uTnBO0en1KLQnhMB28CBE9m8I5F7CtqawHYyRmE4X68dILSiMwnuJSQolEkolkBJBouhqYnD0S4Hv5PYlgUApjVQRKePMA0JQVZIrrl0CkZAK5lOflGae4jLm0iBh1tMvIOXKg9JkmddoMhchpZnDuKed1KQUkDFzFaKLCCNRlSA4iDiSakFoUm7aJ5FIMVc0QkwYrQjB470kekgxB9jZEVwSpcok8DZRyILJuKXXLxDRk6wAMSNNowjWIxW5BckLlIwkGRCmzg7saIRMmC6IGKgGMy+JIAnOo2WBUDJL2kqLKQuEULTTbcqBQFeSGAzOJjQCFAQPBIlRmpASKSpSjCA8ZVVia0upIykJQgIpJImEb1oQEqMNpEShK2Q/q0BV3YLJ0BN8BKkoCpOTPi9JMWAKSUy5UtHrVQQfERoIOy1sAZcEmohMie11hw8OKcSsGhYROjs76lIw3h7hfKAoNSLmakpZVbiUEKpk0K3YWt2i0zUoHRAYoo2orgdhkD5QdbPLt1IFqpKMhlO01DO+SsTGiJagTUHZAbs1IqUpQS7ipzVlp4MxFVJKUpqSvEQIRVFErFf0ewMKI9leHyGDwpRdYmjpLHTZ3hixtWkpOzC/WH5BbgZfUiQoQ8He6S5Wu+uZ6Huep45IgoOTPXRCycuOfhX7J7sBONld488u/yBBnCF37pvu4uVHno/aWVES3Ns7ym83b8cfngVfDvgk8ECBJwc8SvncUG3yPvXHvGbXd4C4EFkvsmKPE9L59/uiMYssdutF3rj76/n7e7+B/XoBgP961T/njuYwf7B5C3+0/R4+NPkkPs2O18Nt83ReItijl3hSdRUvnnse+/QCb1h6OfOyuhQkfMkhECJRJsGrjj6PZ6/ewC89+f/w357yJlY664+bZOPr7nsJXp65zltlZ3KdD55ZMBuL5yQLZ6NC8/Ljz6EaB/bUS+ybZoLxcrPAnmaReds/rSw2DFPevv1ePrD7buKxOb56/Wr+ZPghLj+1l73jZf5B8+956eDZRBIueT46/Qzjz/ZA9IjxN1j5wBoxSsbxzUzC/z5nHHVsaJKlji0+BVzyOYY4a3+e1vtqFvTyQx4XiWJOL+EJF1wmiRzLFHZAex651EjkN9ffzjcvvZL/1P4OB44/h+2ZQlopC57bu5EPTT7Na+dfxKvnX8j7xrdxR3PoIcfldcv/WbuFH/ncz7LPLPPk6hr+7YHvxybHHw8/wAfHn2IUpkQikgvdHy/hsYyLTjSEtEghECnQGyhaAnPlLkqjaKMHLwnB0u1oJsEhTcDXnqW9e9haX0M4mQPklCi0RpuszqOUwrqGiKU7KIlOUvYKiFnRCJln+oUMaOURPkAEKSMoQUgRQUIqQe5MSiQ5m9wLuU0pezoIYpQg8jxpiBFmrU3RC5SOKBNJOiJidhqP0ePiNLcmIUgRhBZIGbNZW/B0u13Kvgcdcu7iBUIJBHIm6ZuQwmCkIFkQKiJ0RASFFHnsCUGhwSaLkIpStggtMBpizGpGpnC0ySOMQhLQ3TmE9CThshmhlCA0pESIUBpJnCpSULmKIxIkQ4iZwOydJwQgmuzLkGraYYEyCqMkooxIr7FBEbyAFEkhUJQlPgQIiZiylFoMAu8EwZMVrISc7Vs+3knkpMfZLPdblR7ncz+o1BKZBDEIki4oe7lxtp7USDEjrrcWhMI2luhjbj9DoeKURId2MiT4iO4YjDJIo0jJobShrR1SSgaLA0LI/BxnQReaGCIiJWRyxImh2+3jnaNNEalrut0OMYKuDO10lPcxZB8RqcHbBlUaVNUnjbYJracNUBRl9viIoFRBPZzgvEcZmcm/TSAGsLZBFT1SUNmLA48pBgwWEvWkwUiLWpzDIGmYIGZKWI15BLKsX2ZQD5CULaLhuq0r6fsO3/nZ1/Pq+1/CR/d+ivft/xhL7fw5oUMUku+843X8p1vfwLzt0/fVOT3RY1Ofs7yJ6hxNc0vkNzffwZsO/xHeXeQs76MQnI3CJu/bfiev3fWtPNAb5Fx8Hv1MD/hIIQwvGzyPf3nwjTy3ewNK7FiaZo20G6rLuWH/t/LGPa/nV1f/L7+38Wfc0dzHOE7zCsSF1qu5ufc0vmnXa3hh/2lcUeyjJ8vTC+5s4xK+1JiRxlNiTzvHv/j4d/DKwy/gJ5/1y7zzivfTSveY/5p+/09/+py/E4kgzx9cb28J/l2rGJ/nvctZ4Bfe9xOUxZkDcr6KQUyRdw4/yH+573fYDNl/4NkHbuBj08/x3bteTxss/2vjj7i5/1Q+MP4Ez+w+id/d+FNGO9fUo4CAxyeP4fyTTCklVt0xPj350HnfPw0BTbXFwtaVrOz75HkXuaM9xIent/O8uSdzz9LddNYu4972GNdVV3DKbVCKLOZw2J6gTQ4p5EPcuhJNtUVvsptVt8Wq3+RT9d38wdZfcEN1NV+/+HL+/cF/xKfqu7HJoS84EXMJj2VcdKKBLLJ3Q7S4SQnaI8saGwzjzUAMDaY0tFObZ+tVSQyW9RPrRJcoOpKiY0gioLTDMplxFgJJeExSCAJSJZx3uQ0pOYKNGK1yy1LwSA3RC1zwCAmhSUgkykCMCSUghBxzCzIPQmpmwThIKXPrFDY3L824DlIzI9kJkpAoBTIKrnrSIveFFeLE5Jl9LXFtS7ACXWr2XjNAdV1WntLZD0Ogc4uTAF0JYmpmvLNAZ47TpnBazW5rSWY3aQveZS+Juh7SXZTUNZQ9iKHGj7v0FxVNY4nlVibXhw44TSIH9El4/NSghWI0bXE2EoLAFGnWKiUQqYspEikFki1wbYNtJSl5TKhQSqNVpK4t2hiCd7mikrLvRIw+y9EGQXSeqlcyndRIqTEq5M4PaQiuRRggJFxMuPEIKUqiV4SYCY0iBZwLGJnw40RUEak10QVUKfEhokTAR4l0IbfIpZBN82KBErnyJSUYIZBJ41yNUaBFhROWlCJt3TJtLUoqvLUYo1CdCtuOMConDdJ0KTqRZtLSKxTRN6juIm3dogqwDqJv6Q/maaaTzJnpdEDCyamdJXqOJDxSaJTQyNISEOgEUitihDaAllCUJQqFTSpzdlRCIdBFRUeVtNtDklB4EekOejjX0Ew8bfPYVWJ6yx//3Dl/d3zBM9duoAyGcmZ49uojN/PKo89jhT2siJiDcwEkwc0rN7HM2qxDR5zmARgki+487r+z9wPwfzf/gp848t8J4Syjry9S0PXwCimKp3Su5bOTD9Imh03unBlNACkEBYazB71oBjy1+8QsYZ0Er1p4IX9n16uYkxU7zZtn72z+X2RZ9fmne7+F79/zt/nT4Yf4n6t/yMemn2HNb+0symXFHq6rruSa4iCvXnwJz+k+iWU9mL39QALJYzx6fbxB5O9aJXjO+vX85rt+kjc94U/4qWf+CvcNjj2mv64H+xSIB01g7MA8JJcqP+8f7lBMY8M7tt9PSGeSmeVZlVALxffv/gbesnULT+1cx0cnn8UIwyTWD7cbjwifndzKzxz9x3zfgR9nSe99EJdBkjgy+XNW7bGHXdf2/BH2nLqRlb2fvOB58O7Rx3jt/As5fvAI5rNP461b7+UVc1/Fp+q7eXLnGiKR/7n+Nr6q95QL3tsUMhshy4hMCoMmEvN5KRTzqs8he5LvO/xTXFUc4Pn9p9EtHt9CJ1+puPiKRrKYbkFMgeCnyELiGdJs1gxHEtEqBp0CaxRCtCQ3pW1qbO2IIXtB6EIiTMK6BE4jSom3DuugFILoAmWp8d7NiNHZmC4mAUR8SBAheI8QAh00wkSUAmMgyYjUEi0dMULVzV4TpERMGolEiIDS5DYfm0jCzwJjSWzI/AclcquQUSwuLNF9To97P7fJ8FQ7G5tEFdDrd+hWFUZFQqtzkqJyJUYKkasXYqYmNRdophZV5IBdJBAz93MFEB0ojTISWweStKTYQYZICpq26aCFBixGe0QwRKshamIEU+bjFdqEiw6hwBS5mhN8yqRo0yemGvyEoizp95Y4ttJkNSSpsuN37TEFaCUxxhB8IPjceqVns/hZ/11BkiglsU0keEfZKRBEQvC5WqAlIkFMKXMVpMxVDQqS0nR6HWJI6K6n3a7xKaHLDt57hBEYFYlh5jsQs7IZAryLiBSJJLQ2+dAJkYnjokZ6cD4S0gg7tSiTmAxrqkEHZ7M3h3MRKWq0DkQvSMIQY0u0kcGghwuB1iWk3yb5RJAKQUTrAu8CZVUgRGA8bij6/SxPLEGbCmQkRYepFvF1Q68fCFORkw8daJsEGkLbEiQgu0RnMd15QmOJJIyRuEIw2R5RzXeYWxiQfIVQEmcfu2TwVx+5gPzojlRozrxRCPawwabYQ8OsKjF776w/LhoSeP3Ci3hydQ3vHd3KX4w+yntHt7IZhrh0llzwxa72EXwFEkElyguuPJHoCc9PX/ZG/sGulzJKDW/Z+DM2w+ic5Q6Y3bx68cXMtMsAmJNdri4vy0W+lJORPKnxUBWGWRAqoC8MX7fwQl6z8ALub0+wNdumQLBbL3Cw2JOrfmInnNhZ72M4Uv0KQg5IEwNf8p13vA6VJD9088+yXs7aZh7nX2N6FG6VPdnhP13+g3z78mv558d+nneNPsJAnuFjXFXs51/v/3tcXuzlyZ2ruaO5P8vDXwBi1sdwNnbpBb527qt4VvcGfAqcdGus++3TlUYlFPvlmMOjN7F38Y14znWf7oiab5q7jjcNnk5XVrxm/oX82fDDvG98G6f85jnbm/RWWNx8LSJJ0gU9ZyKfae7j5679QX7pxO3MTTu45Pl0fQ8v7D+De9vjHDC7+ePhB5nGhoNmNzd1ruXKYj9XlQe4stjPfrOLShR84sge7v90nx+59n8QlMMIRVdWLOo5Trg1Ukq0ybLfPHRr2CU8dnHxFQ3VzJwQs3GabR3RdRFJ0p8H6QW6q0EK2jrgnZ2p9yikjOhORbAebRRKarywuDABkb0alMkSbwhBUUqkbJEl+OBRAXRZQlMjEDiXUFEgq+ygrUswuiBJgRCRFDWoQGg1SinSTGYxhJhn8XWmgHurKUsgJWQV8QIK2SU5iTACnCT5SKU69OdrhmstUiS0UoQZKZtQkHwOFqLVJJFbiZRJFF2XH+lCIISiaTzFxKBSQa59ZHMf7wykajZBGzDG4xtFnFak1DLZ0gQvKQtBPVKURYkdCZQGNyNrBycRsiDEADFmsnpQTLbATUGXGroO5ecQIhBchekeJDSfIBQGAsSoUKUiRkkiEtpcOUhRkLyjdhGFQKSYjfoimEpgxy1SCpwLpBhRIiK0QSqJr6fIqn+aTB/xFFWFDwnXWiIC6VN2KjfZfVypTFb3fpbcxDK3tpWCZjpFmxJSTq68d1S9IodeXrC1OaQ7P8BUJcFmX5IQFLIyQCQGh5RZcKCoOoRplq71scVIzcLSHG09Zbw5RGmNKUt6C13w4H2NKXrZndwYohNIFbCNpdc1jCYtstCkELLamBbYxiGbHklGdFlQb62iTDfznUTK7V22RQpBCFnAoOwJrBN05xcRYpPJaMqYhl7Roz/fZzoeXfg6/XLHBQMbcc7/xenZ+BnOJkh/Hl06gkQpNDd1ruKmzlV89+6v45hb55bhR3jf+DbeNfxLTrg1bLo4bUWBYFkv0JUPwZdJ8OTOE3jJ3HN45dI34M/TNjVrymSRTRZkYKH7BABe0LvxgUdgljxcOF05l4h98X1hAjAIrisPPErrvYQvLwiEAJki33T3X6PRDSd2XMWf9aUd2RcaTQPuryiXKoRAo3hm90n89tU/wXcf+gn2miUqWeaUWwi+celrscnxN+ZfxN+9/0fPu54ri/183+6/xdM617ERhmz6ISfcOrv1Aq9deBFXFvuRCIQQMxn98w6GI4xYfUCikRD0ZJdfvfJHWdZzDGSP79n1ek66NW4Z3cot41tpY/amkPMGddv1PH3u77LQ1zSp5ePTuzjp1ri82MuuWbVGIPjI5Hbe+PTnko4dIBC4qjzAZcVePjT5FC/sP4PNMOT/PuGneW7vKezWi6fHfzYmA6j78Lz+U9APiDivKPY94u/jEh57uHh5W1HinM3+CVOB1h1iUBhd0Ot2ic4jpKcsNOPtBm+z54TRCqV7JJWohxPKokCEBqFUJkwLh9YdtDTEEPCuRQNeJzqmi/CShEREhTIlYmaLbOuQDVxkQkkDSJIPkBSyiHgnIJnsmxBE5m6QQGh8ILcaxYBvVeY32KyO5ERAJIVwEMhGe1AQdQfvNgl1xKaEEJLlXRWikLQ+IEm00xYlEyklXKOINo8rCI8ApCuwkyx/ile0NmAqTRIRpePsmR5oW0lEMdq2FN3cjhajIjQSUwisThhlUFXETQL1KLtvl93s2u1rhbcQQiI0ihgczTCQWkXwQ0xlGCz0OX7//Uhk5j0gkEbjWkfREVgbCDHNOC4tUhrk7AYYYkRRgJxxX5KlU1S5EiGzuq8gkiJIowi2JSZPTAqtJTEq2skUiJQdw3Q8ybK5kOWPOwVtOyFJhTE6czFEQVt7fOtRSLpdRVN7hFSoqsAowdiO6M9VgEQ4h5QGdGIymdJf7OPqKVWvg60tRaXwbkiMiaaxJFlinWcUtiEFpE5EEainE5RKzC/uom/maOwEqSW29djplGqhS7QWrbsI6alMiawKVKUZr4+RKtE2Lf1eFx0cplKElDKHQ2q8jygDGo1vxiQlkaagkIrQ1ghZEoVjOo6IBYtR2X39KwczDwYJK/dOKOOE5Wv5POgMZzcRJUqhuLrYw9W7Xs23LL+SjTDhPaNbec/oVm4ZfoR72iMs6Xl260Ugz2q+bullDGR+wEsEN/efxv5i90NOm3ZlQVeWbLLAvbNtn1sbSMyzxW5WzxrfzogfyU5+oRKAS4nF4wWCRBU13/eZN3D6e32cJxohz7s9KhBCsFsv8mtX/Sgxxez9IPJEgxYKhcQIw93NESBXKZ7ReSKlNDy980S+Z/frudyc2/a0k1A8MDh/KJlXeR41vi5ThEhcfdZkgUJwsNjDNy+9km9eeuU5y//2LvgqfRNPODh7phNxyVMIc57WNLjPQgX8ypX/HxJxumXqhf2nP+x4X/hCuPlmUJfoF1+xuPjWKS/RlaKeNkhZ4WNAUaI6irK0WDXFiDlSEgit8C5hCk2IHmMkTd2w404VvMsz4krQDGFuXuNc9tWQSEJIGKnxTd52TIogE84LNFnhR8RAagRSK4IjE5BVbleKThICSJnwM38Kay3B5/YmaSLeQhICnyJKkPvDo85jVIKYNMSWFARKS5o1i5uErFSlNIrcbuXaLK+bVMS14KNE6UwyJ4EPEa0MWge8LAhW4wlElwnnfpxVtKTM/BUpDNbFrNQVDXYIIRa5/awSWOuRLpO2o5U5yAmREBNBSVIC2zhiiBRKkfyMK6IEUVhCSuAUc7v2sbbyWZQWtNYhpEEGj9YR4kyVi5RlZoUkOii7hrapcV4gjJ8lIZ4YEklohGyQUoDKXhGFVhAjbYLCZD6FLgvaaY2PDYKSGCVSG4SMeBtRwmEnDmEKykIiBARXZGfumOj2uxBhPHWExiKVxNYt3V4HU5WkEIgx0thId14zXh1TdgxKSOZ278P7GiElRkM7iVR9Qbud/TeSzDfnfOtXEAKdXoGLjigdQmuEDSRdwsTioqMbI0oofHB0exXJR6LUDOYXOHloNStYaWj9GMUippzDT1pCBBD5e0kOXXVQFKQ681WsbYk4ZCHp9gpGmxMkFW4ScNPHLhn8kSD4yNHbTlJcuR9TSW7/86NcdnPzKKz5XOZCISR79Rxfv/hSXr/wYkax4aPj27ms3MuVxX52koNKmvN0hz98eSWR6DNmjiFD5s95TxI4wAkU4VJIfwlfWIizu+kfu+2XX0oIIU7P+J/vvU/Vd7MVRvythZfxkwe/nyeUBxE7/84TjH8+vhEazwPLupJ4wYmJB24jJbjuOrD2zPsa9ZBE7Kuvnm3ntJTyxY+7KPLPJXzl4qITDSV8loD1BtkNtNNAWfXw3uJ8S2gkXqkcNBFJoUVQsLC0xGTaoLXFW0AIpNLY1mFUiTIOFyxBKGIbiFKQkqFTFUgSPimiV7iYSaE2eJLqABrrLR1taJuI1holEiIKYoh4l42joswO094pnMsXaPKQXMzJQIAgFCSNlIEQFaIQiOAJUZIQ6BjpLhiEAhFzf7JICSkVySYShiAiiFwZiFFkArQR2FrhlUBpjbMtvrXIUoAXhOgQISFUQZIJ77N3SIoCbRRtcKQoECGT2k2l8U0iep/l+LWkqAzBe1IyiBkfxVRZESo0IScQCVCSEHPSUBUFx++8B6IlRTHr7460rcWgKQqIzhFsQEiJlGLWy5ld2KXUBA9lJQgp5GpErFFGEFxEqJwgJASy6GFkJN8KHYiECxZtcgUqeIspoK09RdkBBCkFtEyIFAk+0To74/QKCtXBxSkgMIXEWktMhta3JAdVVWKnE4TUtMNNylLR6XUJtkbLitp5UgrEIDAdcKnE9FviOCArgSoV1o8RqUArQ2++h7OW7fUN0kIf3e0x3hrjmimyMNStJyWNEAJrHRAodGT9yCm88xQdg0wJowoKLZnUkeAs1jpModBVJ9/oZXZu1ioQksbZJgsYpIhWmk7XYP2YotQo8ZWQaAgKk7h6eYP/8QtjVKm47kDDk5/CBb0o/irb2lmjEYIl1eVr558ze+XhEomHH0tuTXJUtJwrwrkjHvFA0vYlXMIlPBp4tKoZF4tdep4fO/D3+JalV3Fluf8Lso0+Y3bUKjMSBfaiPy8EPOc5D7/cAz/zSJBSZoXIzyORuoTHHy460Uhe0jpLClCPItY6lAwUhcaOA9FrKC0xWrxLOCfQShIah0aQNFBoQgyEVJJEJHlBJOFdAjzSKJTxiATTBiR2Jjeb8BHQCaUMIbRIFfBe0tqIjA6hFIiEb8hkb+cIqpu1mYXP3g4pEXxC6EScSfWmILIqVQQhFS5FjPeklLDO58pKCix0Oszv6TFatVnmVuZKR0CSvM8NHskhQoEoIil5Uh0JrSQKQzSS4MA1FiNN5pA4h4gClQRRSGJo6XYkPgVMWRFTS+s8UXq06mIKR1mSZ9eJyFKglcHoQLAN0gSELmjrLRQaKofSXay1yKRIPtKpBoiUmNjt3B5kJYhESAlVKHRpEOTWLWREKvAhURSaRMB0DNZGPAmTEt5boCQJefpulHwAZYgxmwYKkQMqpQqklGiTKxy1bfBBoIVAKJUd25VEhUDAIaMhJJeNGSUYZbC+wbaWJCRGa3xosw9LmzBFgdGGNBdwk0BCUWjDeHMLUylOba6gdQ+ZPGW/ZH01oKo1lEyorkaVCmZKYLqr0f0pMRpEEakMbG1tU3jHoNdlEj3JWlLSOAfaGGw9IQmYbLvcdjZT2AAxk9VtcZMhxlSYjqQwFSnEfPyUh0KTRCACojLM97uMt2uSi8ztLtFlIrQSYR6NWf0vf2SR6MRk0+IsXLuf2dzgF3arD/335489rLDNHO1Z/dV7WaF8kInHo4HPR272kkTtJTy+MB6D9+d/b4e/UT6KtkQ395/Kzf2nPnorPA+6TFlgi00W2eGzDRg9oiv3oeL/Sah51+gjuOTZa5Z5TvfJGKFPV0ZSSgzjhE9O76IrK64s92dvsvwuwzDhrdvv4WvnbuaJ1ZWf725ewuMIF986VWiUD6RZgFWVJUJGrJPZ56FK+OQAj5SaslthXWBaT/HWU3U7KOPwNuC8RYqAbX2WmxWJpLIbePKCFBx0G4IrEC4BDqULylLR1jVGKHwMGKVomxwEhygQ1hGTwrXMTAIdKYFKHkIBTmQVpwQyBExhaIXNl4hIJBRGCZTMUq/BtYQUKIsc8uw/sMhkc4XoNUJmB+dkA1JLVJFwQYL0iOizUpZXCJ2ye3nMylgxxWyuZ0ApTSklKkFKEaM9ZddSqIjQjtQGsC26rIlhRDIVKSWULogiEiJEClIpqboGT0s9aZhMa/xEsbiri3MtEkGMWSa40+2zvbkKMuSWHxmQpiC4SFUJCJBSQ4wgRcC7QNXNp4n3EiEkUkNlQCgLNiFSIqaAMi06ZjK5koYQPUpmg0KtIkGAMYZe2SdYT5IeqRUJj1SOGBVFlV3npZL4FogOoTRipqQ1ngxJPtEdDGiaFm1KbPJZTKAwM7WoCkWD6RhicOhOwJQanzQhTBE6sr0dclDvAskkVF8w6M4xnTqUrPBmTPIVVdcwOTVFliWBCbYGS4UUnmmAQmuEioy3xvgQ0KXM54PRpGCyZ0mQdFA00xExZqUzJcC5BmenoLos9BN26IhJIJSj36kQJoAW9Lo9lFLEOEaUZnZyf6VAPA5i3xwMaMKD2hvOvPZX2ckzn0+nfwRtdByyJ7Mz9EOgEkUmogqBPGcklxKPS3j84tHkb3wxIVKkx5BNkfljgoR6mGv8geTyHcK5S57D9iT3tEe5sz3MC/vPYLde4HsO/QQrfoNKFLxi7nl83cJLMCLzKG+v7+Gt2+/l7vYICsminjvN60jAJNZMQs1PHXRcv/dbLoqTcgmPb1x0oiFVoGk89bhBVYqyaPFBYIxCVwbbBnywSCRN64gxEJ3DpwhRIAgEKyi6CqLAhYBIAakM3jmEt5TVAC8trm0xSRG8JTqBNhIt8+y39xIhJSEltApkSoAhiZbgDRKPDwFJASGhTYESFR6HJOFSJDowWmbibcyz9xJBCJZERQzkliWhiN7TejClQfUDaI2SCi0SRb8lSodLCnSgM2dISNA1zkWQGqMdCIuQBVIaOiFQyxbfGorK0OkUEKa46LPhoDEIk5BiilmAFD0xeZJQxLSBTBolSnwURN/F+0C0EZ9KIqDKMYN5SehIZFFTdgLWFnQLAUrj3BbJDEnBQKHQHYkIAtWxKDqooqUdJqTMPhcpgnMOVVpIFUlqikITY+ardLua0XZNpyjxPiGTJiaPUjVV0WcyqdFS4RSoFBlPG1KA3mBAMiCVRWtDCA1KCJSR+Xt2LaZK2CZRGIjBZHGBjsG2FqUF9BTee3TIJHPEFCkKfFNT9CBJTagj5UJF0zoKUZKUJaiAkJZKG5KokMrRX94FkwQ+gUgU2iHaXYxODYnK45NFqg5KVWyPtyi0oNQdmrrFuhrTa9AxGwSWlUYoSZtrIyThEZ2EHyV011B2SrQKtPWs8mUEwkjwAukDZdmjrOaxzZDlQZey0yG0Detb0OkY0sL8Q1+sjxck2LcfnvlM+Mxn4fkvILcBPo7QUsz8cHOrgUsPJYwJGnG6HSGkeDq8qGPDp+t7GMYpv7/+J2zHMaNY8+HxJ/HpAlO6Mwxkj+f2n8qC6vOGpVewoPrc2LmGnuxwxpAPtDgzb3kpCbmES/jSYNVv8YeTz/GM+StntNdIJHsDpZRmDtuSQGTVb/KJ6Z3cMrqVw/YkfdXh2d0buanzBO5qD/Ob6+/gw5PbGceaSOTq4gCvmn8BdcxV1iZZ3rr9Xt66/d7zjsUBJ9zaed/7Dyu/wU2da3nZ3HO5o7mfj04/y23TO3hK5wl82/JraKOjJ6tLScdXAC460XD9bexmQAqJSpEkDN2uog2WZAsK5YkuNzboIhFEIDgxc7g2uBjxztMpFkgyEFwgOxPkGe0QYFrXYCIhFUgbZhdQSSRhQ6JrJIVRODeFaIjCo2WBMgJPQUoz3oUDUxTE2d+lkdnjQkektOhoQHlMp4usPMSGspR4NMnbPBMvAqZss0lh4VFFi9E11bJheiqwvE8il9ZR0iM8xMJjqyy1W5iEzGx0YnAoI5ASCtUhxA5xo2G8JQiNJy4LuosCVTq0kRA0SldYV1B6jSwjPhRIBTF2CSESRSC5fq4UIBGmg0gRJbpUZUWxq8E2HqHGeAqK2CJURKaaxk4RrkL5iFQF0RdYaqSD3kILXfC0mNDDNxoXa1Q/ICtFqKcoZVCmQrYOlECaAlHXCB2yRK7MqlVSKmxoiKkh0UWiiTESQ0uUHmFKZOlRFEgt8KJAG4kMORFUUmRlKK9JscRUniSyW3nrJFE6fBspK4kPibIoaeoJtk4IbdFdQzudYua7JLFJShIrHZ1uRYoQTI1RXWTqEVJDpxCsbZ4gOMXIe+aLChdqLJ7QeuYH84zGFmst3f6A6dYm2gTctIXKEDFoo1EWbNNS9kt0aQg4VBFIehupDcYoIpbgDLZNuJDQwtJOVeYviUT0U6bTSDMdkoRjjnnqcWTcruBUgWq/chKNfg/274O774XLLvtSD+hicHbv9Nk4l4Wx8/tI7PLh0a3YuMl2mPDm9T+lThdqjRM8p3cjz+nfRATetf0B7mwOAYJpbPhMfQ8Oj0/hgR97SIx9zVu23gXAb228HY3ihs419GWXnUSjEIbXLb2MvXqJZ3WfxAGzm0qeeXxcChUu4csRzUN0mRrDg+RWv9SYxoZPTu/iYLGHy8yec967sz3MfrPMb6y/jd8bfoqfm/sGNIZPTz5ET2+w3yzwG+tv561b7+Ga8iD3tsf4VH03q36TcJZS1f/gD9AoAvFBfh732eP819XffVT2ZdVv8a33/ytu7t3ELeNb2Q7Zn70SBZ+t72M7jPnZy3+AOXUeo9WvQGReSzrLqej8AgKPRVx8orEJvU7JsG4QUlEUgqRrjA7ML/eorcRvTEhOEhpP8hEtBVJKTM/QTBu63Q5KOlKo0VJSFBqfWqSBOA0IqYguUJSKRECXHZIZExuBTN1M7I6eoqMQUYJRpOhmhnuOqBIxSIROICYokyhNL5Nqtcd0p5him+QqvAhIFTCVI6kpRZkwaKZbBfgSUXqk8lTVKDtEawO+peov0a7A0sGI6gYGcw5vE8rYrJolO/hkkEZlQxxZIYWGJElxnji1qFSgOi1oxXA8wsUK3auYXxSgwDqfExxV4qwgiT7BCkIIFLqTZyw8aCEyOVspoosI46ltgx5MUfPDbJqXMg8kxYAoWspS0k4CijlsHVHdmtjG3IJlBLoaYZKhSprttQR9Tyw9VTcQywbb9EhK4UgIL1HKg/b4GkRHoLQCrZCyIYUCLRRCRmKwSB0RUtMdRCzbqDITzClberIk+URsBAGBlIK2dmhlkCLQ6RTUdYsnorzHtYqyLJFFwtQCT1bVigJEF0JXIkLAuppiANgCUwhiDEgMVa9Cqm1s7ZCm4sTJk6R2AGpMX3tS3aOeDgnOUal5xhsWFwKFNIxHGxS6xLZt9k1pLGYgiKGGSiARNPWUEB2DA5rxcJPRRKKcprvQgSiwbSKJCf1+l2BtVqEymhQgTmuqvqQqDE0IWJclcmUs6JhFqs7gC3ZD+LLC7B6b0izcTV/agPaBLhYxRdbDiJDO6r8QgqP2JO8fffxBpl1GdHjG0vfS0Zmj4dOU/3bs3/C7p34Rl9rZNh66ZPPe8ccQK+LCywoe+UE6a3lPwKfAbdPPPWixd40+gkBwwOzmpu61fM3czezR87x07nlUQrOkB6dbKB4fj8dLeKyjri+sPl0UXxrJ1ZAiHxh/gtWZkV5Pdnjx4Fn45Pmhoz/Hr62/hcuLffzc5f+MPXqRQhhc8nzXoX/DD+79u/zx8P3cWx9h3Z9kjznI/zr1c/zk9C8BuK89jifAw1gt+YdptXq0cMpv8Jbtd5/zWpMsP3vqt6hEyXft+jpu7t9ESomTfp1KFCzqOU66dWKK7NIL5/BDHq8IKfKu0Yf5L6d+hzZantF9Ij9x4O9TCPOlHtqjgosng4sWGyGIGiXmwRc004QoHJsntql0F2RJNC2y8pRFJFBmY7Mo6PY6BAcpSKSKJKHwviUlgQ0WoTWyaqkqQzOJeOGQIqCQODSFAd1RxBgJBHQPvB0iCkHR6xLiiMZbSi2JTiG1xHQiKVmiLVBqDMohi5rEFr6Fpk6UKVGWY6SGlHp05gtc4wipRhuF7kdcHdBS09UFSmt00VItCBAOYoEpEkXZQRoIoQNBEEJLEh1KqSAaXPC0zpOkJEnJYD5LrirVy4mCSLmf3ydCCGhR4GxiJoyFxGNMIsaIIIL0RDy6jGgZaCYgizEhBZo2B8SIFiNbhNF461GigxCBzkIXP5KUixtoozHB4sYVFCXFYELQJSIYlHWUraUzL0jCE9oIHYGPZMJ76OKbhC4SqjuhqAy+FoiYlbwEEVNmM8aoA96CKSMYS/LQKbOkr1AC6RUpaVS3pLETVNmSQhdFJGlofIMNTfY4KbNClAsT/CSiKRDO5pncjkUbEEGhqoj3W7S2QwwFoGlbizCeIo7xbKPKAufnUTpRj6cYY1gwBxhuj+hV84wnDdPGgQkgBEEHlDRMmwbva7QuCd6j6xKtJIhIpzK0ITLdaui0fSRL4LOk7WSrRoqE9ZJer493DpipcrWRZjwmqYiNmQBdVAmt+5hCU7JICpogJ1+o+8GXHxLc8CQYzEP1RZJI3OE5ROCoPUUTLQjB4fYEf7b9odymAExSy59uvZ/pAyoQTbRshuGD1luIiu/wgu/a/8MIJO9Y/y3evPor2NQ8osj84ZKRvzIuMJZE9gg66lY4ur3CH22/HyM0y3qBgezwivkX8Iq55/KM7pM4WOzOEuBf2JFewiV82SClxKrf5CPTzzCQXSpZMgxjDtsVjtoVhjHft9f9Nm/efBejOAUSWmie13sKNjo+Ov0skcg97VH+9fFfYknP8e3Lr6UrO9xvT3B3e4RKlEzjkBPtIXabA4z8Nne1Ry5igA/4+0t8cTap5b+uvok6Nnx4eju/vPpmnt9/Gr985Y9wX3ucb7z3/+XKcj8v6j+Dv7/76zlgdp/+bCDSxJY1v8VRd4q9eolry8sfcULypeaMpJQYxSk/s/K/+E8rv8UoTunKin+y9+9gxJdZue2vgIvek+ATOhkQmhAt1krwiaIsSdEyHE9Rlab2DhkkqioRSqAKiR21+Biz94bzOF+hJLhQAtlVmhQhFbgQs3u0aYnGYESZXZ17GooJMdYM5g2TdoJrI3OLBSlYpAqUnZrUSsxcCVrg5RTpBdJMEF4hq5qUElIaNAmpHUJH0v+fvT+Pui296/vAzzPv4ZzzTneoW6UqzRKSACEQEtgGY4jdDgvS3XGvdEKc1d3GzspgxwZ6dcAEjLGz2nYa20kMtmkPBILttEMYEsAQwBgkISQGzWgqlWq48zudYU/P2H+cWyXVfFWq0r1Vup+17h/3nH3O2ee8ez/7+e3n9/1+xRwhZvhlAgxSy+0KQOgpoSVlyRQrtHLEac1sMcPtWBCCIgRS2m07ToqUpIghIFPF5CMisbXCZY4kk7NAjAVlM0IPaD1n8h2Ulhw9Ugas2vZhSxmRAmQSlFITx4DUIyiPqTJFebLYhuRpfYYQWqxbEUMHVGjZUpgQokXpafv3kJKmikzVGhE1ynYkYVB1T0kTWQVcJSCOqJknREO1H+jXIOPWLcvYDdSRsEm4WUXRmSJBqkwaPUonVG0RviGIgm0KRRaUshQ9gdRkMRCVQhaJyo4UA84IEitUk9C2InSFKQzouSd7jTCCMBSULhS2mgnCSBaJMBSoClpvb1Ft1j1CTiAUdQtRe6Y0oeYdRXuKrmnc3XSn1xiXE9LvULxHSM3Vw619bVQThq1mZgoj1jrylOj8RFZrbN1ScsTOBAqDQqOVYlgOaNeg7ETqApWu6XJH1hPjlClFQFGMStDMLf1yW4wpHelMIIeMFBbtoG7mWG1uOAnA5LdBhl80ZHj1K7f/gBdQGlBumM3C7/ef4B9d+ymO85p3r9/HadqAgFjiY73LT+ImzKp8GfnV45/mmw/+r1zzl/j7j/xV+vwiS3l/wvcKRK6EQ64An7j2z/nH13+K82afPzz7Sr5194/ylub1vMrdg3ksw+GOtuMOL25KKZymNdfjKa+r7qOUwlgm/sejX+S/ufrj3D89gkAihSA9qp94BkKJvGPzvic9/ki4RiMrPjB8kq9pv4xaOP7V8l18cnoEiWKh91inUx6Z7n+aHd2eaVVSHEyWC0ONzoIkCysT6HTi1HrWOt6yU/Inj/8V/+L4lx9r7Xr4+CqvsBeQQnI1HvNwuMo7Nu/j55fv4Psv/Dl+Zf0ejuOKa/GYi/4a1+IxmzTw58/9e/zQy76DXMpTppM/FaUUHvJXOEpL3lK//gtebJRS+PD4Kb7j4R/i19bvfWwF/Nv2/yR/fPH2l9Qqzk0XGo2sGMYRZ+dgAoFA7W7kB0yBrb1QwJCo632yPKL0jm65QcqM0gol2E4EsyaVgBCJQo0GspxIU8GIgq07gjCIGBE0YAK4NcllVKrADdArqmpOziPa9mRZIM1AZ1I26FwjaBDRoWYrYvC4uiGrBpULIm4wpUJXW7F1Sh7RdKRQYYzCmELfTdA3GHtAUZ4hy20CeoykoQJdUfRWMB6Fo+SCEzVlcnQbkNkghEEqiTQC2XYInahaRRAeKSXjeIwQjpSWWKXIY2L0hqZyBMYb9rd2G0jYBHADxgly6ZAiIrLBpxkhBqQYEXrAbGM8kMVT8jYEDzQlW3KeECVS1Qckv0FqgSwe1Vbb4D22SelJJJrdTIgjUhXme4pSRoSMCBQ5G1ZqojILTJXpOxj6ihjHrQVv9MRO0jT11iELDzqiTCGljKIlZ9CNJ08OoaCoxBTWgGGaPNOktk5W+pRh0yKLwueMwWCpGNf9thVr1zHmQ6KYyFONcZaSA0hLiZJSwYIFl48fwqgltasRqWL9SKZftvhektURIlaUqJBaIzSY0jJ0a8YxYKqGaaWIqUe4gkianD21m0OCmDxFr/ErS0qSSgwYrUi+QOlRNuOE3f6dtKWpG0zVoIolxiW5GKJUpJix1jBMGyrbYIxD2RHrNP5UYauGuLl5z/QXO08aa1+gsbcguB7X/Oj1n+JHrv4LLsfPEjhusyufl8+/f/wQ/+nHvoUxdxzHa4+9nyzbntwkXoRq98/6TSY8D/krPHT8C/zLk1/inN7ny+rX8af2/y2+un0jr3H3PunlEkEtH98i8Pk7cd3hDrBcPv/vuc49f+bBH+Qvnfs2Xlfdx1Fa8u2f/kF+YfnOz2pJSqTP81S+Go45jWse8Jc4iSsq6XhP/2EA9vRZWrUAnrDCWUAVwTxq7hpqXtbX2CS5Xk2sTGBtIr2ODCoRRbmp8UYU2PUGgeDE+uc9xuiz9SORxH995Z88aZv3D5/g49ND/NbmA7xv+PiTnv/A8En+1+Vv8A+v/y/89Xv+M76y+ZJn/MxSCu/tP8yfffCvc485y8+8+odw4guXKuhz4CeP/xX/1aUf4VK4/tjjf2z+Vn7w7v/kJbWaAZ9DoRGjJ8XEEBIuaeZnBVWdCXlDiRZTCRAj43VBlh21m7OJS4xSmIWgBE3feVxjsDuG7Asoh8gS7SRhShAliRGpNTkHwhjRGlx9o/0mbEP1ulUhJYNsNboSRB0osaEETVUpiipopcmsCKPAqbvQzRJRNJIMYo2RFYk5Tq4pW5spXDPb9tkDJVcYewaFgCJRxeCAC683pNPCtBbElFmdRjQGpTKVdqS5RFQCoROxTGg9YueKYpdkBLJU2xWJkiApFIroQRSFthbctkroVxLCDFE0tinUZzbQrCilxwcFwqHUdgak1SFBNGQVUWJEKIPRA7lAjpZcLN4HtMmIUhHTBiW2trGUBbBBim2woZBsXcBkROsG1U6MI9TNhJaWrNaQWxAjpjKkfARJIlQhh4SuNFMeQQqKTmRjGMYROw8UPZHNDFJChm2WqR8zTgWGEAg6I52klMzkB7JN2LZnih5ZVZQJ6r1A6CJ9HxBSYHfAp4IIN1qvnGK1WbKzc4aUD6nqmrSeCPMVdWsQ+YASJP16w3IZ0cmBUijRkrOEoulXR4zjQBgVlTXkCKuTNVKAcQKREsZq0BNjLlTmDD5N1LJg5hIzCZwphC5S7DZlfm53ScWQTKGtDVVlMUowiYiqZ2ij8MsVtXL4OFLVgrpxHJ1coTItJWoQkZwkta2f6VS9w+dIAQ5Tx5974Af4+eU7tnawL8Qc94YrzCX/wGP/d0lyX9dwYajZ85ZPzzoebDtObXjePtZkQRTl+c85fDpufE4siUvhOpfCdX559S7O6j3Omf3Hb1tgR834P+5/E7UwnNN7fP3ireyr2RMutk9UyNzhDs/O821fW0rhfzj637gWTnhr8wZCifydq/+M/3X5m897S2MmM5SJh/wV/tHhzzxuZWSTlnzfA3+a19Rfxmm8MVEt8MpNy5ef7jIPmhPr+YOdFRebgUnmz/nkEQX2vOVNpzu8omu47jy/fv4qo741nsDv6T7Md57/0/yZT//VJ2lMHpgu8lMnv8Zvbt73rLqGVDI/e/rr/PmH/xZXwiGvc/c9rth5NCeklfUzpqU/F0opXA6H/FeX/j4/efyL+PKZcf4bZl/FT7ziB7lLHzyvn3k7cPOtU2bA1ju0BzPyNFKmibIbMdWIuJGFEHuBtQq3B2EVaReOzntkVGxWa6y5kYIMJLkgBE/GE9YBKQXSZDIBoQNhOSKQYAdko6mMY4oOUQnIHU4KpBgQJaB8A8EhrEOpkZigiA1ZFYIujOGElEcYEvWsASQl9Vh5TPSSUjQyOYTYpi5nMiVlrHYoUwghkqPGD4XW7KHOKkp0CFnhdm44X1UCPfNIC0JFShm27WViAtmiyx4hTwgpEGUbVIgoVK6iaIlEkadIHC1lEJgE0gjqPYmZL0k6kUuDUZYkNyhV0Eoxxg0+CVISxFGQpxolZxSdQQGpJwuP0iO5KETwIBxCDgi5IBeP9wpjRnKpIGW0toxREE4TranoOo8RLZ2fUO4eRBnQWiHFhCdTiUgokVwMRRmE7iiywe54crXBVB5ZKXJOlLSELKBZkL0n+h6pM6XKFB1IaiTEBVkklE1kE9HGEk4TMIFSBF9IjLiFYswNVYZgJUpLsswYV2O1QjiDqQt919MtC7EI4pTARXynid2EkJasEmVtGPo1vgnkMpFyRtmWbuOJccTUghSAoreJ6kiCz9QzR4wD4IEW5wwhWUIZqM4oYg+qmRhSQbuRKjZIbYlRE6eeMSbSUFBFYivJ2CfO3nWBttZcOjwhhJ6QR2o1JwVBjIopnr4QY8EXLQXJP7j6P/GvVu964YqMR3nCe5+dHG87PKBK2wuaKoJHmv55/bg3LBfcP9/Q6S9w/spnfddSCtfiMdfi8VNu+s7ufQA4YTivz/CNO2/nLnMAbJ0Avqp9A29u37BtsX0aWllxl9m/kbR+pyS5w/NLKYX3Dx/n/3P1J/iu838aKQQ/cOlH+aGr/+MLrpt64sQ6FM/vb36T399sbWdlgdetFrz1aI+Njrzr7CEPtT1RPDcHDZskbzne43XrOSYLepV4x7nrjOrWBY+8c/N+vu/Ct/N187fwr9e/87jnLoZr/PzynVwwB09y64Lt3y6S+MjwAP/06Of4J4c/yzr3/MnFH+IfvPwvUwtHKYWpeP7nk1/lb139cf7d3W/key/8medtdSGVzK+vf5fvfOSH+MDwycc99w2zr+InXvnXuNucfUm1TD3KzQf2TXuIImHs8dGDT5imIgweUxzNzBGrgZwsTes4PT1mmAT96DFesXN2F6UtKSdyysS4xhjNNI2sNqe0TY2SGWVaxvUJISlm+4Es1wRhkHlGUYlSKmTaTqRTGjHTLlkHKqNJRRFDg6kGhkmgUkUz79n6rq5R7CKLBxPxqaUUhSaQ8xyBJAwBiqKYTBb9Nu0yzChFUKJHBNDJkQZNjpoSMspo7EyhmwnaAPTkKEhZgJgYfULrRN8NWw1C2RYeUTrUjTDtgiaMirAGkTXFJ1SlqeYCZSIxKJTUCKGRRZKFJwaF94bJr0neQY6IrLcrCQV8BusUaINQgYxDF0VSCimhyJo49EglqJQhZojJoFWFyAKrC73cbAX7qnB8AkZ5rKjRVYukRytHLgOjnxBaka3CVSM5Z6wZKdKgK09JktVhpjBhlERKxxiPyR5sFckiUC0mfB7wXU0YOzKQrSGsErqWhCmjdEH4BT4vme1aUukoUaEkVHNP9AVcZrep0EZQco1mD2IkTJKuX4KOyCQYh4ZFUxMDhDSw2ZxCNMRpgxAWYw1J9BQdMXUk5EKaFCUXTLEkBM60lEljraHeW8BQSLLHiMBYMtIrqloRkyAViSn7CBVwleH4+CpBHGHKOYprCYxIozg4fw/DcaL0irwuODsj2IeIOVBV99KaipheiCTpL04eDbdb5357d+kLPMZfqgc+uHfKW4/2mWTmvWeOOXLPX2tcAT68uyK90ALyZ+Mmf9epBB4Kl/mxw5953ONOWqpnaW04rw/4W/d+B9+6+/U31DZ32q++2Dl/Ht78Zliv4VOf2j5mLZw7B699LUj5zK9/lFwyv7T6Lf6zh/4mXRrYUwv+/U/95Se0S90CCrgs+aqjfV63mvNI0/Mb56/jn8MKxmezP1neuPxMEF+ShUm+wDdhnoVr8Zi/9PDf5pvmX/2kQsOXyHFa8hr3Mn67+xAP+ss84C/hhOHV7l60kPzLk1/hV1bvZX1DlP+H2i/nH9z3PRyoHQrwgeET/JVL/5BfXL2TULY3T7/z/H+IUZ9foVFK4Sgt+btX/zn//bV/8ZgpwKP80dlX8hOv/Gvc8xItMuBzcZ2KESUt1UzQH04Yo1G6IeTAzv6c035NW+ZoJN2VHqtnxHzMznnLzu4OMWnyFFBFbi1BGZhiT5hatLBELwleYeqBej4jewFeEkXEzUdymtPU+2yGQ5RxFJERYUOuwzZDoS+oaiSqhFRrigmEoJApgKpJRYHOqBSBBGFEmRkxFVIvCIMge0tKA8plhLGUImmcQbtEkRIcoCzOOkQqOCPIMhP1iDUVQwgU5sSyoSRPiQJHQ/KQGchSE1MipYHgBwiWatYgVY/biduE7l4Tc4GiEaGQlEJYRfaBQsHHjHYVIBBSYOQ5TO2Q2SP1jZWSDIgJYRMxBlQWyKJIERIVxCUmG4QopGRBKHIqVEZQdA15TYoC4yIlCKxyaCux2t0Qq1uyBGnAJkUKcxCJnTMGpQ0pnSLI29wMBGMfEQJyEhRr0dWC6AeqplCERrmBIgbCoAk+4IPDTx5hQGWLGCJWNwjhsa7gdhJSKYTcRRnD6fVruJ01OTucrJCmI+ueeGop9KShcHK0RioQCYpxMAaOY6ZuMtMQt+5btaFbD+RRUJzBNYnhSqQ5J/CbbTYHooDN2yDBYsgZlKo4uHtBmgQnxxMxW6qZpEwwjhMpBlQ9I04bZrM56/UpQa2pXIMLlt09TYhnuX7yIOv1EkQLWWFNTRARVQumY01bC7r1Mca9NAejW4Pgd/oP87Mn//qWfHoW8NGdNYtgOLGei/Xzt5rxKC8q3cfTHNpT9kw8cwG2TBv+8iP/PUPx/Lu734AR8k6Z8UXOW9+6/ffAA/CzPwtvehO84Q3bAsTcpHNoKJF/eP2n+L5L/4DTtEaj+HMP/nWmcuu1cotg+LprZzg/VgB8at593kUGwKn1PNT23Nc1SARVkrx+teCB2Y2V0Vt0Yv3G5vd4b/fhp33+vf1H+OZP/sVnFeC/wt7N3733uzjQO5ymDf/06Of4G1d+jOvx5LFt7rN30cjq89rfWCL/++o9fO/FH+Z9w8eftPL1Vc0bXvJFBnwOhYaqAnGEkg3aWtozga4/oWlmHJ4eMrNzYtBoB0pZks5caA4IvtAtC9M4MBxtaNsWLzMmt6Bn4BQhrPCjQJlAPw3QWpTUjMvE/KxifWypqoF+Oib5wGxnxNiCkAnh9hmmJaoKpCzQ2lDyNo+gqhRTGCjeo9sdUliTKw2ikIUh9jVx2rpWwYByDa7eZnikpDAik8WGPEQKkuQ1MmZ0LqAFXZ9Q1qKEYYiSmE8ZpkQzcxRhkFaSieQ8YrVFSLBSkZXBxYQ3kRhXGNeCUriZItUBO58YTxPjWMMgkKaATsiqIFRC0iLV9jtIkbbOUjojEShXkWMipoBKhtosSLkjjZ6cAn7q0cYhJEhdQdxauxldkLpCFkhxhpADSjswBZEKxmhkqQkxkIpF5B1EGXB6F188RoEoDUIumIoi5TVCJnwvKQSKUDQLvdWhlAElBaVEXOUQbs200ZTgKCOEweN7xWzfEZKmzo4cI1jJ5D1xUkip2J3v0V3tyWxQSLIWWOuI0wlhbXFyj2G9YcxbZ7PiM8o6QnZEP5LJ+NFhmoJdzBk2herMKd2VabsSJOZUM4PUgeZcQASPYQc9DwwnCVsVlMkke43V6jq1uI/ZbMbR4YgNgqIL7UzSrxUqb9u+1idraAy7i/OEZSTrxOlqSTdcRDaSphzQb3oKAtUY5vOzRG/ZKIXPHbYRzBr3Ag4JX2wIjuKSh/zlW7YHXmZ+9+CEKDL5pXut+fy4md+lwEfG+/kvH/rbtLLmTyzejhUKniZC8Q4vfR6du73iFfBf/BfbFYzPdT730fHTfM/FH2aTtzcBHs2auR14edzjZbuvRp+/gDx3jnvObkhXP8y1B97PIJ77Cu2oMv/m/DVevZ7x+tWCg8nytqN9vux0hwfbnk/O1xw5v72J8QU+uYby9Cv6n4m8e2Yuhet86ye/g4WakUriQX/5cToNgLN67znv46M24H/t8j/mJ49/kT4/OTlyXy34b+/9Ll5mzt10kVGeEArzYilObrrQsPWCUvWMZYOtFQcH95CbTxDXhsY1yKwIIm+tb/uBYSiMlabrT5mbAygRoQu9H3FeMGUgCUoYKUUyTgPKZ2yTCOsMoidnyTBmZCWReY7PiZwDQvV0Y8DkHVBHyOoEIVpyNMzcDl18GKcXKG2IK4mRMJz0RC9JBgQVAo0yFlN3FNUTh5YcI9pGUg4UadFFMcWIYCCPM3KQDKeZKBTtzg6msrimBW3xq2Oy73D1dZRpyEpAHrA20Q+KmCJVnpOEIGeBESO1O2AMPcMUAY02Fqk9WWSau6CMHf5UESeJoqFsAroyhDIi3EgRAeWmrUWstpQSEdkR0xojJFIZUioYcUBW14m+4CpFFpmcNBRLYSTnYRt6mCJCVOTSQylIqRFFkyJIM2McJkRWRBHwU8boRJJbLUnKFTGMKO2330XOkChME5Ei0tsNyixQpSFnT9NYlOnxYU0aPCVmrNLQ7JCKx9YR6wy+g5N+w3y/EIGhFywWNbqSLE+vIa1AacHhYaKdSbp1wNkFygv6dIVu45HOoBtD7GAYOrSBhKR2Aj9MKO0Ypp60SRi7w849azbXR5I1uLORqgK3qEg+k81VSnee2U6Da0A7CWnrJpXykko5lNL4MFLNLOM4IlxGGcc0BeIUWcwsjTpgakf68QqT99StRcSa4iJJeqyDqqnxoySGHWJ5gBASjdjj9PSLKEfjBSaROYnrFz6b4lnY8YZeJ4J8/kTgX3TccAd7KFzhT9//3fyf9r6Jr5u/hW9YvA0FLGTDnl48tunjC5A7bVYvZW62ReqpCCUSS3z+duZ55Ny/8x9wz5/8DlQzAyn5k0AaOh74O9/Hez7wv/CJ+Qb/XHQVAqIofGyx5v75hgtDzRuWCy4MFV+ymvPa9YxDN/HxxZpHmoFBpcde92LAl8DVeMzVp9GMAfzM6b/mey4e8D13/Rn21PymLXOn4vn/nfwKf/XSj/Ipf/Ept9MovvfCt/O17Zc/4/uWUshkrsUT3r35IK+p7sXnwN+99s+YqxYnDH/2zP+ZN9WvevYvfQu5eY1G8FTzGtU61ieeocuQK2Qp6KpGxBP8suCngtIBazTWzDD7NX4dydOErBQlT4jsKKrQDWt0rpBK0cwbwuiRyWKzxc4Fq26EaNAlcLpc4SePbCS6h1IapI5I0VCiJkmBz4UxbjBaknwFk6ayA34SkA8o0wAIpJvAnoA5j6kUohi06AjjDFkSUkmMrBGsaOq7KEEgXM3QjVvnodzi4xF5ykwrS8wBayWuTgg1o+SERBLKQPEzjBpQjSSEQ0gtIhd82oraNQJdKXwsZBHQshCSQMmCaQJtJZhWhrBJhLB155LGoGqJMokc9baFK1lS6UEFlHAUaQl+IAZFtlBUwVRyq11QhRgTKQwYCzFGIJOTQt0QjBq9tR6mGCSZ5CcEnlwMWkJWBlIhpxFrt7qB7eqKQBtAG3JI1G6PHDxVtUHkTCkTIQtEKgzBU4QnJQ0Ysuzxk6Jx7oZ4POLmlqIjMUYsip0zDboIxt6TckBWAZUc1k7oVCFIN8TYx1hraM9JfOgJXcVsd49+lAyrRN0UiIZ6Xkhqw2x2AXsgOb4+sjizS9V6qj2Lcw7XjPRLS20KUY/k2YhSPflkQbMXWZ4uyWNFZQolW6oWhn5DnwZMawnLiX4jqW2Ltj2hD1xefhSjG4rJtDstrdhlFKcoZ1E2k4KmH0aMhv6kw4hdojgl1/6Lyt72heZ6XPI3Lv1/mcqtneDfNVTsecs7zl0nyBdRq9Ptxo1r9ip3/PjRz/HPj3+BmWwQwCvdy3hz8yUIClYYvnXvGzin93mlu4uFmiF5tmD1z70Y+Uz449P/TWXZ3pl8kczR7nAb8a53/Tjziye88S3fgjo4h2wa+ve8E/P7H+LtJwfoLHn/3ulzLwBuFBwPNz0Xm57zQ8WbljvcNVScH7f/epV46BavcrwQdHnkh67+JO/YvI+/87Lv4m3tmxDPkNHxqOHFn3/ob/Gzy39DeIbi9I8v3s5/cvZPIcWTK+BHhesP+6u8Y/P7/PTpr/Nbmw9yPR5zjznHj9z33Xx1+ya+5+Lfo88jr6tezhurV97Wqxs3XWjIdhvm5seJFAWnwzXCoKm0wnCdk25gvcnMXENzxtKfBkRaEXJHcQnZTAQGzCBJucK0a3YXEpMbJI6YE6enJ4ydh1Ewih4fB7TZY3O8JE0Vla2oK48KiqItQ/Hk1SlKRpRNyFhYpYHd2R6lmpApI+MCkSJOSdwZiRQjSlq0OkPKUDxbJyi9Q0YhZCLTYIwipgJ+2CaZ5w5tJEUFRD7BSk0JEj8knAApNKIY8pQRBYpeo4QihDVWGXKIyNIiRaFpK0LJ+HCVnA3SJESZUWgoJSLlRM5l2wImLaodwIJWDclH4liYVhalHNpmssoU6UlGIdSEqiLEQCweJXYZuoSgQemOkGtctYNSE1MaGDeegsXUESUVUrbASCoKJQw56a2+IwS0bLe/s/TbO8BCIgiMfoV1CSEcgl2EGillgyiOft0T00jj7qYoTVEeLbb7p7EUMVCQCKFJyWCbGlFGqrpl6AbavUw1H7FWU+8sqNuWbrVGlQnbNGgWTMfXKKkl2UgoHdZcoQSHjxmnoNYat6PpuhNMqbDnBsLoUbEmlYLRBsySLhdmZyX13nnmZzVhXBHjEiNatBHkMQM1qtJY5SiNZOwm/EbQzgwuVyQliLrHLmCzhjJ4EJrd+Q4SGMyaAGipUFJipGNcFry6SikFExVplCQTKbFniomSNpi7ekR1RPF3b3NV7vB5UW4E9P3B8ABH8YbZ/i0ep+8aKmyRhFsgLpUFDiZHlRRL61mZ2/MO7k1z428ZiJzkFRQ47j/C7/YfeWyTf3z402gUb2pewzm9z9fN38KXN6+H8uSi4D57nldV9/Koza5E3GjLejIFmEriY8MDXI7HvHfzQd6z+RDlKbQyqkj+87v+A/744m03skPucCt4tCUlkVHIxyZtpRSO4vJJbTW3C0N3wnt+91+y+/PvQAmFkIoSA5St89puuEkhyrMhttLPy/XIlXpkFjXnxop7+ppzY8Xr1ttVjmPr+eR8w0Nt9xmXu9t3/vusFArv7j7EN3/yL/LNO3+YP9R+OV/VvoFXu5exp+bIzzpWfAn85Ys/zP98+qvP+r4fGR/gA/0neHv7pQghbqyEBD41XeSXV+/mF1fv5L3dRzhNj19tfzhc5T/69PfzN+75C/y9e/9LvvORH+J9/ZNzRW43br7QUIaEI3NItb/D2E/YeoZzmuWRQihH6yZcs0C7Qt0kdJOJ/QBpZLeuOWvOsb5WI0Si2dMMsadfXmenuZsQN4gYmcJEiWsqITCmoUTQZYG0ktme22Y5lJG5aViXQEiQk2IcAiTFTBe6fkTkHbKS+BzBDqAqtCooXYEEVzmUgL7vyTGQckNOEklNoWMaNCJX28kwkTxlKBHrJDHlG8NRQvmR5A39EAkh0s4lsgiyV+hWkHNB6IosLFrWFDUQU8a4hLF3kcqKHA1VNZLjEolCSY9UBpEzqJOtiFq2GDcilEc6gZt5+lUh+YpYFCVabOMgW0r2CD2hxR62iig3EpJEKocQmWlMIMx2/6VAV5CSIEWDcxqrdwk54KdTCgZlagQRbQK5eFJKKGOQwiFKRtmIRuPjiFYnDJuIc4JpHEgRqsoyRI8SAaUKEnAzTQyemOb44IkDGGXRQhFNRREgK0MME/PFDlWzrfzH8Sp1vUfhhJIXhLEQBodrA6LuwGuEkFQ7DVoYun5N9BmRBhazHYo8omp2WS1HRACUYDY/z2Z9mRgKxmlkHimxBhmx1QzJDNf0yN2JUgJ+XZNjZgg9WWRsowidJ5/vkSFhDYSicHbbtpdSIo+JLDJq1zAuAy7tMnUZe75F1EtyNtgyApJaWdy+wS8zh0fH1NUuqgJSzegFpTy53/MOnwsFkHx0fJjvfeS/e3w43y0ii4IqgnNDxQOzL3xr3J63vGY9w2TJRjv+YHf1mXaIlwJPldROwJfAe7sPAfCLq9/cWqo/kQLnzB73mPM8WmjcZQ74lr1vxDxxewHL1PHTx7/Cx8YHOIorsshP6q1+FIngLneWr27fxL5qPp9veIfPkVIKJ2nNA9NFfq//KL/dfYhI4kfu+24aUVFK4dP+Mv/PR/7OM96dvqUIuFqNXLMDd40VpMefs7OgkfD8lUliewasTWRtNtw/22CyYDdYLvQ19/UNX3N4wFtOdnmkGfjYYs31atx+/ou44DhJK37y+Bf5yeNfxN7I+3lT/Wre1r6Jt7dfyhurV/Jzp7/BTxz/wk2934P+Mt/2wPfyY6/4ASrp+IXlO/jl1bv54HD/Y1qgp+M0rfmOh3+I777r/87fvOcv8tOn/5pQ4rPmh9xKbrrQ6KbrWBZgWnyfWLTnmIZIKQqhIQVBEROi2jCkI4y+lxgzu+0ZUBOSOX1cEaYlblfSrSNRjsQsWK6v4/3IjjtA70KKESUytnFMIVA5S0gZ4yaKSmjRELRCRFDCYhWMjEgdSMoxdRmtj3GxIheBamuUmAgl37CsVXgRiKlHaajsnGHSuGqg6D1qF7cC3JVE6pGU8o2J54TSDUV6SAOuqQlSs7oSoWhMo4kpo6MllBGlNM3CIBWUaPDjVVAK7Q5IMeBjD6lGiAFpNbEklJgQcSBFiZSKtqpJ2ZDKBi9aCuO2SKgsMk24PUMcAmFtWV6LGGWwdY2uZgg74WMBZUEkoq9ABiQZZSwlzyl5xEi2qe5WUkpivUmYKqOdYOgFIgeMy1tBdS6gNDEqtAQZNKiAD3mbBVJAmQ1FVDcCHQ2ZTBwiGMU23EOSvAA5Q1KojUWYQJGZHGYoNUEeaKxFaJBSU+RIv5mwdsYYN4yxkPsrGDujPiNQcaCYGcZ0WCPQ5pjlSQ14xo3GhAo5F6iZo+gjmsUCArh2TsgbmCStdsQQUFKR9ZJ5XZGTwRjJ6DPKnRL8QMEQ+j0kI1VlsKZlmm9QNhHGFf1pxdhFXKXJM0f0A3IIuJU3c9MAAIUDSURBVIOGLBcEMaCIUBJHFy+xf+/LiNqTQ48Jik2v0UvDwZ5hZ3fGMG7Q9Xm6VeHc3TPG9a2fGL+YyQg+Pl3kr178+/x298Htg7f4IviJxYZ5MFypb00ReeQ8v3X26JZ89i3ls/7u2/ampy6uLodDLofHn3c/v/zNp9z2KUuKpzq+CkgkXzf/SvbUHIhPs+Ednm9KKfx29yG+/cEf5P7p4mMOUrVwvKl6FV83ewt7es5feOi/eVLmwe1GEoWP7aw5P1ZPOnqaqNFZPjedxs0gIKjCdTVx3U18ZHfJqzYz3nyyy2vWM165ablajXxod8mleiS/BNqqfAk8Eq7xSLjGL61+C4lkrhrGPH1OBekD/hLf/Mm/SCqJ8XN0MBvKxF+7/I94ubuARLDKHWfk7uf4Tb5w3PyKhu0RdWE40Wi1x8lwBBnysE30LqWQEIiVIMuaYk7I2RDnBpRD5o6UIvNzC9xM0sdCd/0Ki3nNtPHsz/eobUXoBwq7SJlQCkxtMBaMqtF6jRRztJnjy0hTGmgiToLThVICaSyMY4du9hiDJ+eEyh3Ziq0TlA+oeiTEQikDMZzD5wLTBqkcqYyMoyR50LJsbW0xFO2RMpOZMNrjMWRRthP7WSH6iB8gpohRGmsdWkSM0OQiiP2N1QpT2HTXaKIG2YK4SmGO5OhGO1cihx5RNKJecLI8RqUztPMZPg744NAmoUTENJoUJ7AFszDUMpO9REhJ6iH3mqI0QjmUC6TcUdi2fxEKJUmkFPhekKSiMoYcPEIM5OApQjH2gqwVQmVKSihpUFrTWk1MhWwSQiU0LaUIYpyQqiKljMCiZCF6QdVIcnSUkJgtFtvk7yDJpSBURBSDEBqcR+SJIgRSamLqtjkiqiDUSAyCXCYqY0lthNwxq+bksBXnx5JxlSEMS0gKqecs9hTtbI6qeqpmhk8SUTIlbA//5Fco0yPI1NWc9eaI+a5g6PZoraVeJMLaU7IlZ0U126MkkGmBkp7NdEhb7zH6DSFYole43cT5e89z/MgRppnoesirESEtbS2o7ZxQRfQkyKcDs8UC18y4du0K/dQz9okuXqBfX2JnscNupVioCtFrVkenL9Bw8NJm2ysPD/rr/NlPfT/v6t5/owXwVu8ZDDLxnjNHhNvMilaWrYWmvBErPqpE/4UO/rvVPM3x8Xz8pd7efhlvbd/EDU/y5+Ed7/BslFJ4V/cB/qMHvo8H/KXHPTeUif/Xxf8OKzROWNbPcnf5tkDANTcSZcE+wbbOZYl7IQuNJ+xHFIWPz9c80vS86XSH16/m3D3U3DXUXKkHPry74nI9POcgwduRTGaZNs/ptV0envPnRhL3T4/QyIqL/jpn9O5zfq8XmpsuNFyeEbJGW01kJEyeFMBUEYOn39TIAoEK6TLSBqbVhAv7VLXiuD9FRCh6hcGRRKZqeqSwVPWMs2fu43R1Bd1khC6I0COkhNJijMCwS1NbwqCwtsckS5ATWI80iro6g4wjvTyhFhbtDTlVCBPxfgAFMk8IZ5BKE8OAUDVO1sSyJBe51UZ0HUnN8NMG1xiEFojUoKtMVTlyTsScSWJk6AR5gOgLoYcyTVjXkHMkTeBaQwrbzJFm7hDKkcsGkz3GVAiTGQa1TUKPFWHq0EqjmznjeqQ/Egg9kcI1oqhQQiCFo2rl1jEqJaTJCDRhSlQVhGFJXFnGjSSNlpwLzbxBthADCFWIKaJURJiMcILoewSKUKobxUdFli1CTsxqQymSKa3I4xypJM7VVJUkh0CYCq5x5JTIekCUFkRCIJmChNjgak8OCYQH3ZKiJpaAkBGJo+RIiYokNghRo3XCujmJDTlLSILkM41rmaaARlBKg7IRSsa6ObIpjCFT6YaSCsacZXY2YcQCKxNZKRbtPsPQk6YlKVpyTIhYY02FrQsxBMpKovTEmGoEPT4FOIFUNE6+ls3xNTIVlbWgJoYhoXVmOAK1l0lSoE1FihuWR0umEJFo7LxGhAx+xIqKarGLC57kak5Or3D9kSNcC2lIhCmQGVG2JRVYbZYcrzYYU2gXFbosXsgx4SXGVsBbgMO04fse/mF+c/O7fHT89G1TZADbO4NPKDJ2guHV6xkf2l1uvfG/8LvEW473eOPpDupGobExkfftn/CpWbe9O3mH50bZJpn/27t/hNe5+9iupNwuB+NLk1IKmzzwT49+jr9++R8/LjPhifgS8bdru9RTIB7taXoCJksOJsdaxy/c4SWg14n3HhzzwKzjrUd73D3U3D3UXBhqrlcTH9pd8kjz3JPL7/AZhjzx8elB3ty89lbvytNy04WGqe9iON0gaofoJ3Kp8PEQxhpfMsYFqoUAFYlR0ncCWzeEvEJNLfPdGaSRaDeEaUQmgbEVQngkZ7i+eoBGGuo6ImUCLRHFME2KOEUwHdPGYauAnyqcidR7iVwEgkzwG2IQ1JXGmQk5pK1DklXoekEsE8VLUgEtI0o05GII6QSjArqZIWQgWcEwZoTKCDnhTEUg4ppdcugJUVGSogBGOeRcMZUJp2uWJ4GYR0wxVG1ASYuQgpg2IDUlbFBVgzWCnAwgsdZhjWHKM7RMTOOIP92nlCXWBIzbATcRo0aZFl8GfA8hebSeKBSCb5EZ6tZjDZRZIdlAOQadDCkm/MYwX7yMmEd8mpBiwFQZaUdibkBkyjQhEUirmcIIYoEQnpwyUu2itEBKhxSSEAK5KGwViFOD0AFKhTaWIkeKhkZ6vKxR7BM4puSEYkXwBWk6xjEgxQzjDFIatGxJIiKFZIwrrK6xqmO9HNhb7BBlQrvI2A9YW1OZCmhBRrpuDaVBKMPgT1FCU6LF5yXKNKhiWF/rSbYjJg9+hxwbxj7iKkHtFgyrJVJlrJU0rSP4mhCPSL7dFlSDRcddqrbFNQbfS1phOT7NdBuPLY4ds6AvR3TrQEkjaI/vJcSJap5ZzM4wLkdESEx+w0m3Jk8TWSY2h1uRfU4ZJxVaBNx8QbdeE6YJmTNaSHJqX7AB4cVEeZwL0GeuVqFEhvyZpeh1HnjH5v387Mmv8TMnv/qZZerb/AL3ynXLa1YzPrKzuiWfX4D75xuWNvDlJ7vsTZYdb/ia6wckUbjYDGQK8Y5L1nPiXnMX337uTyHJ3PGceuG5Go/5v336r/Crq/fctuLu54rL8rGbAZ+NAF6znvFg233hrQYEHLqJX7vrGm873Oe16zkSwbnR8ceunON6NfEHOysearqt096dU+A5USh8cPgk/5fdb7ptnaduutBYrq4xZk9etpC26cclSaSTRF/jjCSVjBUbstEIb5FkApq0SVRkQurIG0XVzlCLaxQyMuwR0glKSVq3h1aFcbqKcDuYVG3TO5PDaEf2kNi2KenqhBJrshi37Tmpw0i71QjYhpiPyZ0mrfcIyaCsJweBMZrkF1hTmIZDbJUpRW21HL7dOiCVAcpEGhaMqUBxdOGILAIygqkKLu8idMM0bnMvpBJUC4WShaIEWs0IfkBR2HSBEiT1jkVKkFqjRMDnsE0Qx23D8ZSnrXewOnHaK5zTyLzE+wWzeU0phhI6jNtFpXFrh4sghBGlIMSISBZdBc6/rGHY9ZRxa43rN5GxG0FotBakJJBxwdAXpMqgC1JmkigYmdHMSKmQSiClQuUcRQiMNGQfySIhGQgpU8KaHDZQWooFqeRWZ2NqKh2RQqOE3P4uSZCywkqHEBFZJNkrovAYmcmxQ1YzVKrwYUTrRDNviEWSY0KWfaI06CioFgv6cST4gsgG5EjMGiEXmJSRyqIqw2ynZRgmsprw/RJp9ijVgDUK21pM3kVXmbqZyDHg3AEytegomM3vZhw6VKpIJWArjbaBcTlhTc0q9CA8qh7Jcc3QzZjNdzEKqkYzqSuc2TtgeQXGvqe5a4EIhuVyg6Cw2ywIOjNcW5ECKDTOZeZVhUDiux6hKqRQ+GUkuF2EuT0Hky8kuRSOU8dHxwf4rc0HPsuZQ/Lx6dO8Y/V7jz02lcDFcPUzIVsvkp/vEztr9r29pbt7agOnNnC5HnjNak6dFfdtGr7+6lkGlRhV4v37pzzSDC+uBPLbAMFWDP4iORxf9MxkjUS85IoMAJWf+jgSCO4aKnaC4dTeAgtvAV5lfuvsERsTefPJLqqIxwqOs+NZTuwOH95d8un2TsHxXPng8EkyBXWb/ng3XWhMwwo3F3Riw7hOyGDIQTCcGoSKTHPFMAzYQSNtoBYV+7t3cdRfJjFQxAgqo4pGakUME1Lt0g9rXNklA0n22wRs0SJkpLKOYViTksSHRNs0DMKj6JimiFVrpJoh5A5Sd+ScmbmKopcIwM0LQ79EINHGY5ozgKSUACWAFOSYUKbBT6cYdkjJQtFIs8PUZ7QzaBWJvsOJFtkKfAggRlQcYKooOUJMtG4fxMSQB3z0SOvRpaatNGY/UsgIZfCTx9+wdG0XDSerDVIFZCnMFq8BE2iiRcsV63WFEZJhXCPTQF3vIQDve5y1IA05rhilRwaJkYYSNDluiLGgceimBzWjP02UMWFsRUmKLEDLGsgEnyg2kXIidgJbZUKMaNsiTCIG0HYkZYuSCpQliYEyQY4a4+aEUJjGDldNhLHCWQklMsar5FxQakKofSSJqVuglUSXmiwV5GNyGlF2m+Zu5A4pR6bOohDE7EFaklcYVRHikr4/gVDTNBWjUPhhCQpyWDIpidHgu8zldU9bz2jqGaMIVFoz9NPWzjhopE4slxNy3EObHoJGNBEhNVNYM4YVoSSMCHSngYWsSaFmmK4zMVHEdkWpMY7WaVIoCNUj9Bztz9EdKSQGYxTTZuL8vRc4ef8H8P2Aa1vqFlAb3HxGHCfqeocQCsEHnK6wxSB1RZ9OeeShh2jmX7ytU49OZS/GU/7jB/4K79q8n1W+SZem23MMflp6mfjYYk24BW1TT6TT24JCsg0XXHQGnSXzYPhjl8/zqfmG9++fsjR3AgdvnhfZAXmb8nSOXk+8u9vKmv/3PX+B3+o++Jx76m9XXJZPezTZLHn5puX088nT+DxJsvD+vVPWOvI1hwc39ndbHB14xx+5dpY32R0+tljzqfmGSeY7p8fnwCfGh+jzyPw2da67+cC+KlLt14ydQFlQamQ8EWTXU3yiREtlIGZYVIYiT1n7ByArtDAorchqJA4TfuPAnUEUmC0MeSnRumPIK7SQaCMxUjGt4rb3vwS0Fihd0QiBsoI+ClJSZNlT0rA9JsU5fD4BHxDlLDH3qDpDUEi1wIdCQWHUSCmRbDyy1FBqKuOQOpKGhA8DKoPWEqZCMBO63mMcA22pcLIi+p6+6/HdROoFUwq4TlA029URW3D1DJ96nFJMISBlQZCRLjEMc5QcGSaFNIYwgtUTOS4ppYYSEKJif18wjYKcIsY4fBwp64ykIqmR7COlNJRYiHmgiAolJaWK2zv9ciDlCNIjhcFnSKNHlUwXA0UVmllNZTNj2KClRbpEzh6RAa+xqmJMG6TVSBlAtESfGDqBcRIhLdpW1DPPZuVJkW0KfJ/RxqKMRjASYmY218iSWa00JRdE1WGVI0XFGDNyKMRUgzJo04KIhBSxpiaHkaqCYeppd1v61UQ732Hqe8gNUhtCGogpkIMBm6n3ZiycZ3l9YMoG62r64QhbVUgrKTmwGgdyCpg2oqqRElpC9kSOSbGnsgcsForlcoVbKEBgXcHMHHNZc+X+60gBeTNj5RPOwfyeHepmwcMfu8Y0bKicQ9vItWs96Iqdg3Mcl4voNlItIulwQ4mKIgTaCaTQTP6U+c5Z1itPjj12Vqjt/nZl6ouQ7XRC8MHxAf7cp36A3x0+Qio3JuEvwYtSEXCxee5iwReCDHxkd0UdFQeTA0AVwWtXc9qo+Y3z1+n0i6e3/VbSl5EHpkucbV53Q0X0EjyIXwDunx7hKC65Eg55xF/j0/4yF8M1phvtkq+rXs73X/izVMI97nVCCM7o3dvaBvS54tJTZ7rAdlVjEW56qveCUcS2FbPTkT98/Qw7wTy2nicRHHjH1x5a3rhc8Ac7K+6/U3DcNJfDIdfjyYu/0MAM+AK+15TcQlJUzYhwEgyEUVIGiXUOLRyiqoleYotEtxWhCEQJHMzupfcDAccwnGDjNmCmNhZdbfvTpR4pfo8YB5AKIR2Bjs4fIqVB1o4peoSUKDmiTE0KGyTHJEbSuNVhxBwpeUYKkcplsthg9A6Iihi3qdskzTiuITfk1JNzYD5viXGkKAc5E/MGFSvaWYUUEPwMKTXSeIxRJJHJYiBrS+1ahK4Zy4Z1P5AlFKPBOKTRKFXIsWE+U6Swiw8BYTdUTpPjDqen14EahaAbJ1S9oXV3IwVs1pmMxFUjOYMQEq0MxQrIBSUcFMvge9KmohRPTJFhyLQ7jnDck5VDxszQB6TIzPcqSkggE0Jogo8sTI00Cwa1IUwDeI9SAoImBotWkmHypGCQ2iKkxPeJMCooM5QR6FLjYyCVFU6epRjJuM50/WVKXBDTQC4e6ecEtUZYQckJow2qHAMRQU3btpyerokhQVT0oUPoQhoLQji6/hrGGYoc8f2E0Q3khCg1WhViGil+wlQLrHVs/HWUVhipKFEzjRkpFcp4YqoQQTP1IzFNVG2FlCNJbojlDJQaJRI+waKC2EWSimAWKFmTNz2ikpQFLFeHrE8TYnZC6kdS0AxjYPAbej9y9uwF2v0Fq5MrHF7xyDInTYLFzgFSFPpNT13XuMUux9cu4eYV9W5Lzg6lb03P/q1kG7AnecfmA3z/Iz/M7/QfIfPSvgiJAmcmx/Gjabu3CQ83Pf5c4o9eOcc8GIrY7uvdfc3Lu+aWaUpebDzoL/O9D/+3/L1XfC+vc3ffSCW/U3A8G1/xkW9jKp5Y0uPCzB7FCs2X1a/m39/7PzxpVeMwnrJJLwInqc+FAuYp9Bmfebpwcivapp4KAVfqkV+6+wpfe/2Ae/vmcc2D25BBy9ccHvDG5Q6fmK/5+GK9zfS5c1o8LavU8enpEq9y99zqXXlKniKd6Kmpqn38seVMdUDjBFZnZB1IYsQ5SdW2COsJZclytWY4MkQvSSUiRWSzfJjYRQa/Ioae0iekakiTR+lu67oyGEpQxL5h9IGkl4SSCTIgdGEVYAoDfjzFiIqUtona3VIjfEVRGaUqnHA4eR8mH6BLjTOBVC6hlCf4I7r+IkM3MGwC/aiwTYt2EeU8QlqMchhjMbZB2oA1FehE3xVi0CS/YexBaEcRHttG6v09zEwzpMDkJ6ZNQnqHKhXC1JRk6dcRUWp8Hjg5OmYYO2CiDDVF1NQ7O9h5xe6ZlnqxYRpheUlw5cErXH9kzXDScvXTI92RIo6FfjPRjyuMMZjKULCEMCC1vxGeZ7crA1YRppFqR6PrgUCmnc/ZObNAyUwpgRwDKge0kPRDwA8T3WFHHgLjpqCiwRlF7BXeTzSuBqW3y58iIlRAiRZrHdEXSpDorKn0ASVrUlpSWYOWIzmvwB5SZMT7NUJkio84ZZFlQQ4tUiS895wcn5LyROU8pg4oY4mx0PUg6FFCI1IhTgOV2WG90aRJ0biKnCsYFWWCaTxhdW1JCg2mmjOsJ9YnIyAoRTD0Ft97YoBUNmS/IWwmclGM/Zr18hBEAlVAZDa9Z8oCxx5n2z10yaQYMcpTNzX1/kg2h4RBUVUNsUx4H6kWoKzn6PgSZ+864L5XvBYrZ6gyp0QF1XW83+Aqz8GZA6brVxBmzdCPXHrwGg9+6H6uXbz+wo0ItyXbgL0/GB/iux/+u/ybze+85IsMAIXgDcsFNt/0MP0F42o18XsHJ3iV+Owa6MuPd3nr0R739FsXwjs8DTeO3V9bv4dv+dh/zo9e/xlWeaS81A/q54FN7gklPmWRAVvHqO+/9A95wF8klEgu5bH2qsvhOr7cJpPu5wkBnB2rp32+ACfW3z7jpYCNjvz6Xdf4wO6SKPKT/pYCwU4wfNXxHt/6yN18+ckuTVTPj6f0S5BI4nf7jz5tG+Gt5qZXNOb+bkTxxKng9MhadahUY51itelpHhUjiUweBL4+ZW9xjn4zUaVI5WYUFQldIgiPM4bgVyiVMVIDAawg+4APBRkbsl4Q4pKoM6iCcQtK2ronBC0QsVDXensXSNbIEiAk1hO07hBXecIUKEkyb+8h54pNKEg8rg3kVKOsJeWBTE3JnhIK/alBtQGDQ9kdFODTRYoaMBqUkLTNHgJFsoXNKmCKIRfBOG3IQpFVIEiBM5KcJhAeiaLfdEx4ikxsTnqapkVVCophTAWr9XbiXxzn7m7ZrGvi2KOEo+QV5+aOphIUExm9QwuNHxNBSfxmpFEKUoUUmSJGctGYSgET0mrCNFHVFq0lUmr8IHES6rkGVdNttqndafLUzQ5VlSgmk1Ng2EiMlQgRSSmjpAOZyEEiiyaWDWHyCC1IJSAzTN4hsqGofZARoXYpakArR46QcqEMFlsrkjfgMiEFSmjRZh/BiiwlWSakNFhVUUqkX0aSnHP2Qku3OkZh8MGjs9y6Y2nQsiH6AtMuSpwg7Qpj9sg+kLIkTRuCO0HGBe28htKQ6XCyIhuDqSBjyaFCWlBtIAcBPhKGyLzaZzrpUDtzxLRBtRlzVhD1NdKwT6W3mpVkBOPoESYTwoDwFTnAJz7wad7whlfystfczacvfpL5nmPoA6oEXKnolyMoCCEgREErQa4Vwb94cww+Oj1yU9stZMN5c4CgUBB8dHyIH7j4I/x294HtBrfLRfOLmAdmHU3UvH41p4kKnSVt1Lz5eI83ycwnFhvee3C0FXje4cmIrb7gfv8wf+mhv8lFf43vvvvbaYR69Omn4c6qx7Pxyelh/sQn/jx36QPOm33uMme4YA74g/HTLzkx+DwY7unrp7UVCDKzul1WNB7lhp337x4cc+wm3n54QJ3Uk76DQDCPhrce7/HG5YJPLNZ8bLFm84W0632R8M7N+/jO89+G4unb6G4VN11obDbHDBnGvES0hSLB6kIWEaUkiQQ5YlzNFHvmsoFiqbWFrKjUjF6uKEiM3sEqCayQ5SxBH2KFpKonplhh68TpYcTGBLqCcgpklDphvqvxGAY/UWJmHZYY9ggco6aJNDmM3CMRsVrhp4jV+3RjAL8k5xprFdNKkEONm217+5Up5KCQ2pJCT60a4jRglCHGTGKONYnoHdEnxnKMSLuEIaOExveRaYoYp4hRIMQccs/UbzD1LkIrrBYobQiDQqbA7gVFKBE/Ofqra4puaM8sCcOMwffMtUW4A+p2F0RPSVsHqG5Ys3CODPhQCGlAi0JbQ8CQRo/RjlImUhyws71tsF+W2L1Idj2UjEi77M7P0B33hMFR3Nb2dlptaNsGZSy2Lni/IY8OowzCTCQiKWRIUKsaryKEAmqNSiPRt5RiECYCE1qPZNURUqREi3EzUpoQYo10EzLvs9vsc3jSU0aPEAuatiL6TLM7Y3W4pIiIbQ7IOeDSWZh1aCvxgyXF80h9Cc2Ins9Ruib4bRhhliNCtxg848bg05qU1/jBoErNzkHL6iSisiCFYXv8phOMc1vTAj9Qz1pimvCdQusBrTM0lpQhqYocVlx4xT2cnFyl31wmRocogfn8PHqemLWa8xdakj7i6pWecAoH9zkqVfPJT17kwssucObM3Rxdu0yaCgjD7OAMq6OAqxUXXn4vh1d7xpMlTIZ2fnv2Yd4Mb//wf3hT291tz/IVzRuRFBLw7s37eMhf3t7Q+iK5wNzXNZyZHLOot60DtxlJFD64e8pHd1acHR11Utzd17xqPUNnyb2bht/fPyE8Tdr2HXjsWJ5K4G9f/XHe232YPzL/Cr5h8bYbbVSfwWJ4Q/1KnDDwWZNlgUCJR6dod4qQR7l/eoT7b/LGxouWAq/atLhnWPV81B3udqQI+NSsY2kCf/j6Gc5M7ikLJoGgTZo3n+zyutWcjy/WfHSx3urB7hzuALx/+DgnaX1bBvfddKFxMvUwN9TFMpVIqwO2jvQDLFpH5XboRYccBXq3oaoi0kz4sCaXPVKpseqIYiLo+XZS2mREt0K1FSlERNkFM0EaqNo5MS4pOqCEJKeEMBAng7MO5AazqwglINxEa+aMm8I4zXG6QhrPFEeMWZDSmjQqKA6jJVobNiog85qYr2PznFJ6shbMjCSWfYbuBKPnhLLG+wJmTikrlEw4e5aoFePyiBQEKVtEFGhZiMmwOl4jrWDnrCMwo+837O5JEpHCiFQZqTLDVIhiwPsj9E5h7I7wnaG2jqEvjGPGhCNmi4aTzqOEwxpJsjWq3lALzTg6RElUjULbmjII3I4gDJ7KQAgNJRSQHj8VlCuoGlylCGMijhtEE+iGiM4OLSsqqZnPHOvxkJwsfhhpqnNUrmHyHX4ShGFNJuBTi96J+CkxrTVSnaU9q8kUtLZkElKfohRIW/B9wElDKkuMtsQIQngOTy4ixIwQJNYKYuiRut1avtpE5XaQJEQeKDaxM6+Y+sBycxWJxRpNThVNu4vPARlPSGKkdZIpZIyrUFbgY0LkmsoGtA3odIA1I0IYhMukHKnbc0yhQ8saURr8FDh3/lUsT68ijKbkijANmDrSzCPrvuP0SBPHhNGaqmmIE2g7UCrBkI4Y1tc5e/YuXvnyl+NfJumniWk9IdrIMn4MaRfc9yV3ceVTh8S1ZRwCyRWuH56gm/OoKjOGnvmBom7qF3BIeGFZlZtwiCqwGjs+On768Y9/EVxQRAFTJK/YtHzF8S6LYHjj6YLfOH/9tuwaKAK8yI+J1h9se1QRvHLdootgf7K3naD9tkRAn0d+afVO/vf1u9CXfvRJm1ih+fLm9VTCPu7xV9gLfMveN/L29o0c6AVWqDtajy8SbJa8ajN7RpPkpQ1PCgO9rRBw5Dy/fOEqbzva59XrGU/noSUQNDcKjtestxlDH1+s74jG2QrC39d/nK+fvQUjPjO1LxRiSUQSY/asU8dxWnE1HHExXOfT0yWGMgHw5vp1/Km9b6QW7nnN5LjpQmP37n3WfQLpMWWgqRNrqWkWGh8zU7qG0wK3v89qOMHUDTGMZCLDtGY2t3Ql4mzG6EJOHqUKos2ksrVY9XGDVpYhgzEjUVlyTkjpCQPILNGiR1qBcwKRNTFmStJbbYcs9N0S0yacqJh6CRKUOECwxtZ7aCWIqt+22NQrjMj4aYPMEj8aoEXKiXaxDXuLUSDdDqkopL1A119FdkekUSJzwcodxkki2Oo7chxp5gpTO2DESZBWkktFEYekKCjCEIpGlgE30wyxJ2XNNBpSbAnNEmsazMLj8h6H6xXjGJE5sjioKQm644Z+7Nnd3UUtZhAKAgkmAjWuCUBBykQcB0rYOk8ZeYaSFFNXGMcVlXLUZw1tbPDHMA0FXUG/iSQUaUpI4eiGDX0X0FIjc0OrK4SbmMae7rpk6iRN09DsVzS15PhkDRrMXBCiJKeCoEVJQQg9Up4j+IQsG3QFIi8wlWW1WlGIlCIhZoTcYX/vlaxWHbHfgBZYaxjHSIorsldAIAqLspZCRJa0PZ6mkWa2h1GSLCakCtRqjxg8dndg3GzwZSQTKHIHWyXGPjFNFq0W5NKha0UeJ45XH0ekhpxAuxGhBN3Q08xaKrtDf9IxW8xZjgMpSlwtyfIUV2tyScTJ0ac1Wnh29ndpvWWZlwQZcW7ByfWeWODe1x2wOSosr49YUchOsTo6YWd/n3tf/jL6bknfv8TFtl+EFwzBdgXj7r7mnr5hFvRnArhu4znCEwky86G9JS/rGuqkeMNywXHlGUXiGfSqd4DHjvtMwfPkVhefA+/avO8pX/rjx/8bL7d38/b2S/muC/8PXm0vsFANn/Fqu8NLjgJ3DzU7/uldtLZCcP+0z982CBh14p1nDzm2nrec7GLy06fMCASzaPjqo31es57zgb1THmy7L+qk8akEvu2B7+VV7h5e715OJS2r1HGaNhzFU7o8sEwbNnlgzNNWv/SEi4tG8ZPHv8gPXPiP+ar2DWjU81Jw3HwyuAbSNVSVscZgG8U8VCQpcW1GjBllIIpjFvNEHFfoYqlqjQ+BLDcsSouYa1IMSDkixED0GtGskG5GDANFFqQLFAlNNIylBpkgNiAnVl1mJte46ixlhCEe0Z16dvYDQzbbQLlcEUNBcBbvJ8gK6w4gJUIeCH6NSAKpRhKJLDSVPMCXFZQeWxumVIgedGoJMWJsIazX7DQHdF2PUnNk5UibjlIUfpqwrkIWSVXvMfoOssHniU3nqeqIrhwhatodjTaRUAJhqGmrbXCeUJ6cA5aKtrGs+iVTSbgmUrdrTKkwVY3Rkn6M20RtM1FyQiqLURYf1mxWA3tnDohxiTMWrWpiWaGjxZpMdlCJGbJfUjlDyiumaSRjKMKSk+L4aodWFlc7cuw5Xfc46zl//m6UqQh+opSWxbkLjFNH3FtidEQIS99lRDEolxAxMvURV82pGk0MDh9Goo8UsSIKqGSLdlu9iZKQJkWMAWvA6IF+gOA9IY+UMJLCkpwy8927AE+33FBEQGlLDoocMqVUVG3Be4GrtonwyUeMSaSiEPE8KkdcuySGCiEkWQRcMzF1hSw91gSmfoNSu+S8LSZTLoigWOxGcpKM40h/vMGpBUPZYKoFYVoRWFPQFFUgZbQbkMbgo+JkeQ2tZtRndzBp2LpqiZHeK4aTCdU5dvd2GVOh3ZGM6w396ghrG6oDQbN42ed94t/h9uLuvuZrr52hjZ81JAvgRXjdPLIT798/4Y2nO+x4wzddOs+Rmwiy8FDbcd1Nd4qO58Iz/GaxJO6fHub+6WF+afUuXuvu4+sXb+Oc2uHf3vt6DIqZrDhv9h/3utvPZuAON4sEXruaPev4cHw7CcGfhSQLH9pdcuI8X3v9DIugn3G1RiDY84avv3qWq9W24LjUDNvGwhfJd34+uR5PuB5P+O3uQ8/p9ZHEL61+i3dt3s/Xzr6cb935Or5x/jZe7e7BCEMiMWVPItOlgUTm3pt438+h0OjZ3WtwlSCKhK4EdXOOsVwheI80EuEKZVIoL0hKMKw75vM9tJqYJoEX0ISe5PdBSWQUpCKp1RnWm0MWc0cKkEtDVWmKUNiYKWJOdokYJdMIKSg20wpnCpURZFdha0vyp0xYJJ6SLBCZ+p7iBqbOc3DujfiSIcwxZknOGd81WDdjvTkmjAW7M+HFeaRwaCLoiGsyaUqIZDHZYU3CbxQTEUomFoWSLXt7BwzdCX0XKCIii8VozcFiQcieaVlozjiSP8UogTYFpzUlg2s0ewf7HF3tyAxcOz7GyB3IEaRDqIFcDKYWrMcAUqJFoaSAVAu6IdBUCStq9L5F6BklrrdFXogIbfHjgBEQiiDEhBCKKQZiHIhR4JxBAUI5wjqBNMzqXSpzgFlM2BpkjoS4QWiBJGF0hY+JOAWSB+0EmQ2mgoAnY7F6DyEjfoio0lD8tr9Ymj2UHohZkP2aVe8p2YIcSKNDGstmMyDsgJYZYy05JGKMlFRYHq2w1mCrQl3VqOxYDkc4VWj31FZb4ydiaJHsM6yv4jWUCJ4RaWZMm0gKI7rtGTvQ2pGCIBfP5As5QjsfMEoQhhYtT4hTzRgVmYY8jVQzh5tviNTIIHHVPlPahg1ao5HWEMMKKSxpiGhbmPJI7C3a7VM1jzD1K6JvcUrSh1OELUjTEPwJZjYjmIxoe+p5oLF7z2kQucPty9lxq8V4nAalbHUQnbk9+6ufjizgQ7srPrqzBkAWeP1qwVcd7fHqdcuvXrjGoZtu8V6+xPisY+YoLjmKH+Td3QeRQvLXLv8oArjLnOGr2y97XHHxY/zcLdjZOzwf7HnLXcPTi8BhO34sbzch+LMh4GI98MsXLvO1h2eeUei+3Xz77F1jxdkr57nYDLxv75SjOzc0njPr3PPLq3fzy6t3M5cNb6pfxTl9wFE85Xo8YSqBTepJZE549g6Lmy40QvEUErkI6lqwc3Cek+uZ4C1tpSgy4KrEaa9RVSJuKlw2DEegpYNaUlShX4EInqIVwtUoofAh0dQOfzLHOIj5CJ8KU5AsFgLjIusskV2ingmgoqkhUVA2YaoVpK22gLlkGEf8GKn0hHIaaTPRD/jhhF52qDIgtMTYgqmXpBCoQrV9bzkQhx6tAmEa0KZiWM7pViOtzZz0hyjT002O0u0ikgdGQlBcu/gIrrIIFdiZN2gr6Hxk6pacvesVDHGD0BtCLPTrDLolmUI2M4b1QFNntLIoCfN5S8iK1B+z6RtmOzOkguVxj48FPxZmi5oxa1wzoGzBB8Gs2aVPx+T+GOMCSbRopfHRoi0YqUBGNn5ClUzVWFp9wEZ7Fs4gskDGwHxvhu8NcRohS3bmLVM5JYQNw7BBaIMSgnIUWeyeRSkY0galE8jC5AvknhgTWjdEr5BC4GNHihkpLeRETAXEBlEWyFQzhg7XCKRZgayom5qQgaSxCoSRdGGrPZm6EW8G2l1F3Wi67hSrPdIKplBI/UQRkTg5rNZIXci5R2hLP0a0XlNyQWpDdwzFKJQdkU2PjIpsLGIwKLOLyIqsPBRBteMJSeDSGYZoMVVHSRpiYZo8tm0R4wFSRQQCZzNa7qHRmBpK6HDVglV3iisOssapM5QmUcSSXAeCXVLPE7WvSMEzO9fhQ804FMJw/NxHkDvcdrgsWYQb7Q+fNWHMovBw2/OBvdMXU/cUsN13/1l94VfqkQLMguaPXzzPO89f56Fm+KK86/iC8oTfM5NZ5m0K9um4eZLu6ce+MHt1h+ebAq9az54xPwO2QvAXZYCmgJWJ/Nr5a7zlZJc3LBeoIp614NBFcF/XcKGv+dR8w4d2lyxNuDPOfB6sc8+7n+MKyaPcdKGhsqOuJdJqKl1Rph3WcUUlLaenl5FFkbKh2sYMkLs9tKjJfoMvGZkD2Ra0a1DWo0yHrGas1z279ciw8qjkiaGivcfRnZxQiqEfVqjSEKcDVBgQaQKViXlCW8s4BeqZw2eLZKSqPEpayiQpMiLZir+b2T4hHGPwVLVg8IIcFgg5EEMkTwNyqHB1xbxR+NGTpUUgGMcVtVL4KUEOyG6GSBJRAj4XhNDUc4XQCSUEKYzkyrMeC2lS7OydQSuLSJmpH1ivOo4PPXW9R3tmIPlDKHMQE7P5KblUrA5Bln329u7lZJWZTgdUnUllTa12qeaWTESaGaEfsTNNiGumlBgGQeUSOVlSFmBHnCkMfmRIhjBmnFNo1dLWM1bdCUobVuN1jFKIJKnkHtot2NmbM2yWJL8mjhGMoa5aQkqMPqGsYj1uGIaAUJKQoBRHSiuM3iXGjJu3YFbEPJJiJoaEshNSCIzabp/FESnOEVKi5Q7aaYxUjDGgVIcfa3wH2ghynJH8SA41ygq0LqzWp1i5i9ANbl4Yxkuk0tA0c5YnV5CyxdiazbBBhiV+ktRzj1QVwzTRrzRVfYZeeGbVGXzpIa0w+gx9PxAHSbPTECcHBebt3ZxcPmYaJaay5Fw4PT1E65bpZI0xDq3OkMV1QiyUZElxpBRBKYad86/BTw9Q0DT1GcbuGkV6iqhpFobZvMJPEuFOSVlz9uA8148qwvgwsVz6vE76O9xenBkdr17PPlNMlG1v9Ud3VvzOwQlBvkTsOAXUb/5qFne9jD9hIr+nH+JDH/sVvN881ib2dK+7w/PAnd/xJUOVFK/ctM848QbYmPjiHT8EBJV578ExKxP5msMD1E3ccREIbBG8fjXnvq7hw7tL/mCx3v4Od86BW8JNFxqVU8RYkM6yGjrEeBVbJMhIVbcoIylDph8sqh3RzUiiJo4DtuoopUUmTQ4Jo0ZEFRj8KfNZQTAjpsi4garJ+OWMtj5LSRtiTNi8IJY1m7hm0VSYJkCQFB1J0TCdtmSpiKbdBtD1NUZqZOhp5o6sl1h9N6gOJRRJJLKINHXLOK7oTz06z2jbPaa4ZnVFkwaLmk8smjNILoMUaD1nHA4Jmw7n2q2TUkpoaajVjDH1THFC1zW9X1JkQNSWdYz4/pDczVDpLMJL7rnXbttjtOPqxUukyRNnFXk4g6wEKW0QOTD6GaKcorVGxJG6fSV9f0xdeZJfIsVASOD0BXKsiNMxQnrGTtLUEYFCF40PS9JokVZQYmCzNuzsCrp0lZQnlJzhqgYltpPuVbdBEVkfTxAilEQMBV1pitCoSkNIbPoRoweqtoGkEdEgxR61vUBIHm0mxlXPap1JKGrbkvKEswKJR6QIQmCcohiNMwO6KPohUopGSUucIkorpFCEMFFZgyCzfzBHOeijp1Dw6YjGnWNzekIRLcoOSOGpW8POzi6rkyu0M0EYEsQ9lNrgR7l1+tFzYko0zFidTChp0XVGac1meQrBcOwPMSojwxl2m4ZmnqhmidXxCZW17B0YfJAoKZFhxhiWWANFDEhrUSYxbRyzasbx5U+ipCSWNX2XUU5BylRzh48aa2aEcUSonma+YBos5Ivo5pR0Gwa43eG5YbPkK4/2kE+4M3lUed63f/rinSQ8FQXC1cu4130pu9byDeks99gVn776uzw072Gxi3SfcVSbpo6+P7qFO3yHO9yGFHhZXzOLzzx9KxSOrX/Rp4YUAR9frNjxmjctd561uHqURx2q3nq0z8s3Lb93cMKlerjTTnULuOlCQ7iBMHRMocP6Ct3kreA1OqSqUMnhWSPqFdlKSvKc3W84dgsQx5hiSWPGyzVZK7yXVBqiL/g8IYIBFTA6E8IIvWS+C4PUxGnC1hJrWpSWkDOySIQs2OYCl45OcLpgi0DVGdPOEDaiiiaJjLWOJI8pUTHljJKZuhbEVCHSWXb2AiVpclCUMKPYgFA9Utb4sqSaVQzdioODc+TTBUIPGGHQGJTWJF/YrDtcq1C1RiiLKolEj5WWmBpa5pS2JsSRg+Y8XnYICn6VceYATEQ3iXZvwTSeUFWFtg7k0mHshGGXqrVb9yRvkSIjjUKKTLtwlBzpxhUy9TTNebCJedtyeHyJ4HtKcci8PcMODyfSJqMk7O7NKUmQs4BYELJhvdqQuoakEv3VqxhpmO/XIBThmsc2lnRtAFNjnGKcFO3uATFMVNZR2ZrVcs0YRgbfI2TG2QpKJEdF3c5BDIg8Z1yvyVGhFy05Jpq5RlYzmpnCGsXJ4SlC7VA3mRwmbBMIfkAmSGWF0seQMsrMMW2BcULqAcocqTUhJkpOLE8PiUFgRYvQ4IxBcxbPKSkFbCMoJIoPDNOIFRKtF4RgEHGG90vQiZIFp+OKJD9CHvbQJpLzgFQORMKKBiV2iRSkO6bIQE4Sa2pkBuMsg5cY51it1sx3G4pcoWKiWwXC1ILMTJuANomp3+HM2V2Wh2tkzhRV0bRnX6jx4A5fYAR8xgP/xl39IgorE/AvoSJjZQIPtT2vuHyRk//pnzz2+DkhOCv2+YqTXaovfQv7/95/invdlyCE4PTwIX7mn30XDz7w29vf5s4E4Q53QBXB69bzm9r2tkoE/zzIAn5v/5Q9b7n7WXQpT0QgODs5/q3L5/nkfMP7907vBP59gbnpW6Per/FRYeRASGty3sMZizBgnKHZzdQ7itmuYN54XPX/b+/PemzN0gTP67/Gd9yTmZ3B3WPKiJzHypqhq6qrGzV0C3HDBwAhcdFSI2657g9AX3ADXEELREsIiQsmCaGmoKqrm6KGzMosqjIiY/LxnGPDnt5pzVyYR2ZkZETkOZHumeEe+yeZH9mxvU3bt9lZ73re9QyJ+9PXCeWIkitCUei1o1132O4tdhuBVj0lWpo6014H6hUUlXBlJomMXzq0/gpNdY1ON0i9IzlByh7vFO7Q4ecA3mNaSS4ZXMNpuWeZCm5K+NFyOkiCezwlKC5T654U1xS30Dc3tNUTsodQbrEmMg0e6WuIAWIgBkV2ng+++SF5aQlREMZImMFqy/b5U+qnLdFmXFxIZXws1q4VAoNGEnjsmpTDQppBhwatVkThkV1k+7zDVIZx/ojz6cAyzJzvDF27oe00ul5YliOlTLR9wk3QVA0ubQixxvmANmDrt0l4sjwzji+wCXSpUbpGG4sbFm62O579nGK9a5nmgAgdVnTIqmEMM6apaHeGWguu3tpQ9zV+EeRcmJfENGR8Mkwnh1syDy/2nI8zLko+/GjPN/7g67x69YDIGisblNJUBtre0G4Lti2kCMFLtJUoA5KCERqdW4T3MCrCoLDGIERAsSKKMyWuaJqGpt1ia0WRj12yco5oGQjSUffXKGuhVMjikVo8Fs2bRCkSJd5mjiPzcksuC6k0aKuorMZWlt21oO4S2TxwOHyb6TjTrp8wvlgxPlRAIHiPbQOmHqj6SCDiZ0XdzSzxgaJmlGiQNNR1xuUPOc/3FJ3J8oSTmW6rUUJh1BchapbF4mdDSQKhNHXlkHIil5HMQlESJWA8XgppPy9+6bj6k52meJyY+7tXB9JPc+/7NzSrxH3lHuMFKf/oAyEQRWCyJP3u73D6n/9PkXcHqm7Nsy//Ov/Bf/c/Zr1++y/75V9c/NRYB8PN8sMH232/DJ+N1ravKcjMP35yz9lEyhtWrT3Wb0h+6bTiv/3B2/zKaY3O4jPVOvyz7LUDjWgjsYzEuVD0zLB8QJo6SmopWjEtCdIKLVtmV0OyCDLd6kTS98h2IgiIY4UIFn/YwbKiW2+obYfKLUa05HjN0/YrVLYhKkHWAa+PzN7hxgm/SOLpCcpoUhMgRfqnNfHegFsx3CoYO9IZ5kWTyMQouD8kdGXIZsSlGeELUg+4+YiPEVTCsOY8LZAdUmT8vKBKjVXgg2Y8Rub9QJwk4+SY48LiPPPpxDiPLOOekjxdu0ZpgcgtPiVGd0+2M7oOSCGZwj3LdMSNB8I8ItItURzpOkfyhvGgGT5oOR8T58NLbNVR9Qvj8kBBE7nFbg7U1RM23RNstSOGE8IlnD/hlwHSDDSoxiBMJi8TvblCqzUI2HTXGFtR1RV1q8gkcgEtLatrjaoCtvNI/Ti9e/2kwtRgbKFtC1Jmqk5gbKHfWapGY02mrQV1Y2h7RfQLpi6kMuHjQEiBFDLplLHBIrMkupq6bWhNz/p6jaoFRUhyUMQ5oqgpMTKcbglesiyONIOUipgN7lxjZYfJhjJvcGNDxTWkjEiJIs8oKZAKpCpEL9FZo7RA1lfUpqara4QK+GmkahSr9XOy6AlTIbkd/TUo6bBNQ9uv6PorGntDv3pGdhvStMJNgmkU3L5wyJyIxeO9p7IKIZ6j6RAqIPM9RS6k8xG4ZfDvcvvhh8xTRdVqinBUVuGWM25aIXVDv/oyq+45bdUyLQ0xfba6EF38cALYeIPJ8k/cXRPwp/qbfx5k8fH2oPAnL/CCx17tQuC++Qc8/K/+Z8SPXgDw1pd/i1/4lX8HIdRlU3BxUR471OnXyP/xKjOYz2Ah+I8iHk9G/9GTW5zMbxxsPH4LQR81f/v2mn/vo+c8XarLuvIX4LVTp6xocCLgi2BVC7wHpWeUFoyHmdYYfBq5MlcUXpLcwGoNS8zUSmFsoViY4gHnJJQRozUlC/ywRuo188OHWNni0gZZH5ByAm847AUqZAQKqRqoCvPkQSQSjm31lH194vRwoviO+CogKTQ7g3wCttqgWsfhbkYVi6lX6DZDMozHE3V9zXyWaJUJvtCYHqUbjJJM8xlbS1arFXEa2W0bRuexTcI0NSIVvJ8RJSLEitX1hqWMpBRRJlBUoX3yhHM4U+eZZAymgxwD8yKQVLT2hlreEP3Mdd3DesT0BmULUnh8bKEkql7ilgVdNSwusn/xEa1ZYTaQs0PXipQEWrdok5FCEJ0BqdHVmclNaGvJWiKtYBo8bWOZpvI450HWxJwIS2KYJFUjqE2i2zR4d8RPEVUXjLX4xSFkZjp56qbDKs3qac1p73BWIIUnyJkkIQ2FIjpIGnf2VJWiaiu0EdhqREpNjg3LMNM0Cl0MqUykDPHc0G1bVH1LSZp5FiiV0LWjOLB1RSogSeTYPA7SG15SxEzOFVauETRI05LTnixmQg4IWZMXaLorEoXkCqFMjP6BKtf0fcN4SphWYJtMCK/ot09oVpKiA35sUSsDTaR4S3YR0zynqSPKDEg1o5TGzxPT5B5PbVRFVi1GQbARiqHkhbo7MsyCrr6mrgXWKtp15nTy9OY5H373A0JaQ8mUPGHM6x2bX3zGfHzB+6CdWdTnJ23qe/5gfUYg+OXjijopdJYU8ccxlhCCgmT4R/85/tt/SP/3/32ksfzCvOZ3kUQuAfbFxVtz/VqPG3R8nJj9eSLgo2bhHz695e++ekL1Y4b6/TgSwVtzzX/rw7f4g82J39semVW6pFN9Sl470PAu0lYtqgoUFYhjizWa6RARouC8RMjIfnlAJolZLVi1YkoBicBYzTRdo8XEJAp1VWG1I5sZ1dWM58z1l3qG4EjBsbpRDJOnbgVqOpD8GiU7qp2n6AOkSPYZ5wrD6ZacE2hNThOlS0wfNZAt25uabiW5PzvysqEsBv2lgpm/wP0HEZEFPGvYrg2H4wmdDe16RckSVWYUmlpKRN1RtrCkhNUVdaNIybJEB8IABZkKfkxgE8oWUikoI7EicjwqvA8YEx9TyeZEXVkKkuAOZAFGa7Lx7J5npJSYOnE6n1jGmjDP+LSgbU2SK4jQtgXUHgRok+m7NbObH4fhGYsUCV1BkJ541mg9Q7Ko+kQu1wg9cZ4WtDQsY0SqgDGQpKBJhe2mJyfPeBzxeUCZlrpd0cjHwuZxjNgqEMPEdH4PHxtUJZFA8RKKxROpdEccE2GW5NjTb1qatgEzsz82qBrQB2Sp2b+faTqFsi1FnFn8R5zvLe22IpcKiSPlBO5IKZk4apRtEaphjHt0brC1Zl5qlBEgJGEpRJ8RprDMitVVIc4LzmWUaZBmZhlbqs5hbIVQCvKRaqcoKVFVK3Q9UFZ7UCvSudA93XDy30BZuP5SZjmscctIXUGyGZkrompQ2tGbJ3h5i8otpQikmrFdwPmBpn7K+eSBEbfcEuYOu/Zctc/Zbjz7lx4loWphmBK6jnQr9emsBhd/6QTwXjexqM/fptqpzL/cHviDzYnfetjxq4f1HxfBf+8PISiA/+BdHv63/8vHVCud+NpNzTdXkXi5/XjxM8xmyRP3Z6dNARyN/1ylX/4RAe92E//o6S1/588RbHyvO9WvHzZ8aWz551d7vtNN5J/h6eKfltcONAoFLRRKFpTSxHomFk27kixRkpZI2zWYNuLHiZh5HI6XMjFkzoNAGkfdbZDDHYHIHDTDcWGttwj9gkiHSIrNDbiiqM0158NApTrqJw2lZPJSMy8d2JlYJiSgmgUZJNlLQBFLQBpoNh3+bB6HvPkaTUVdWvbfEjCdQFeoXnC+O6BrkMpgagklUK++wDTN5NgwuoiWjiwbRApYu2EJHhELEoGwDUZaFhUfi5w15FSobMeSXzBPC6u6o1q3ICU5KtRa4SP45EEdQY94t4JqROuG03jH0/qLVI1iGU8II1lvK6RQeOdJGeracJ4cJdSPwU0B230BxAuEFGShOc8Ro94hqzNS7kEnojcEM5LFRFaFZvWMapmJxVFVG8o80GwlplIouSXlV/jJYGtDjJnR7xGx0HYVUrQ4HxlHT4NkuHUQJU1vEVUhR4URj2lXzVVNqy0lFKraEmJks32HxB0xS+aHiB8KblwoOmNXEqiIWbKcNf3NCAJciMQFqnWkJIiTRNgjSnqEyuRcQSkUX2O7iKofGw0o0+LmPfOpx/aZxT0OSlxGQdVZ6qaga4ko1/j0PiKdaLaKbncihDV+2OHnmXaTEHqmKoIkErKs0bUiiyPYLdoMHD6SNLVGtBadPNMRVD1RNYVx6InLhKk2jHtFcAopBaURtJ1H1SfOR4mIFoqnv+oYTxMoj0iKnJZPbUG4+Mv1UbPwUTv/Zb+MT00RsIjMP7t+YNCRv3l39ac6bgmAj1OpANok+a+/uuEXTiteNAsftBN3H08av7j4WbIJ5k/VdP0whcJD9fmpz/hTBHy3m+CPTjZ+8ptvAsE6GP7ey6d8tZv451f7z9Q09c+C1w40tB2IMZMz+FFhVaHre+o2sT+f0T3kIqlkRbdtCCET50xPB10geDB6QcQGWRlkUIRY0KXg4oiRBlMZipCEMtGJLxCNJOd7ConhOCHEBltFSg7MtxLZrjCypn1HMd+Cn0+07TtIe49cBZRIfPTRic1VhZSZECSrt56gxB5nRrQN1Ksap08UJMKpx0HcaovIHkPD4hZKjIyuoMQ1ksLiMvM4Y6REZI00A5lMLhlRa3SzkGOiqAUlBLZ+3ByHrCkEih4QdUKIBiETeraUOFOvKpbJE8OaiOG9l3+IlpKmbxFyRuiREndUlSQb8VjtlVpy0XRtQykD0ymz3dwwphOlROpGYvIdoRhQFiMf28yGJaGqGkTGJ0cMI0Y0+CFQlMAHSPFEcgGMZ/V0jaXGL4mwGJIT5FSIS+a0d4gsWcaRqu3JojCPEp0rgncII5Ay4o4DMVt0bcE0WN0hgiDGDWFYiGdPVUMsA8o0tE0BJShaY5sJJTWFx+A1GTCyoegz47THqEJJYNqeZYgoWmKqeLhNVLZDmgV/jHgfsO3IMmZKejxl8R6kfEWeM/G8otYLqu0gOZbBkpyiXbdEZpAN47QnDnfkYkhLZPP0jFCCvn2KH0Gwod8pCh8ihMR7Sb9N5DLj3YY493SbjlAWSvWKxhqqThHygRDWJPeUnGZiXBBC0OqfZ+EVWQikzqRwOdH4PBLAqONjR5TPuSgKX1+f2QTNrxw2j2ljP+rCLkAWwbO55ulc8Rv7Ld+68fzjq1eEdNkQXPyMKPB0qVGvUZ9R+Px0nPqRPg426usH/tbdNab85G3fBQIFfGlseTbXj7M3No9NWz7X7+FfkNcf2KcFMRjqxjCGmbaWlJwR0qGExVqFUh3T8ljnYE1FzhOkColBVZ7puGK1rsjckU7vYNVCkjOmGanoSUtNypHxXjPLPUYpat2ABNvXLOMDxB3G1Mi3JtwR4l6TrEGXjN1BcLco1cKuQLnHjIqr9Zcxby8cXswk/YIsA6vnFTkWaI40Ysvs9jh/ADq6eo3QjmrVs0wjx1NAF0PVQ/aScZgoQpBSYXWVSAGEDNRS43ic8ZHSiBAjQkpm1xIWQbfdE32iXq8xNKSzIc1nQqipm4pIRtRPWaZbjLL0qzU+emJZcXMN86TJpib5mUzDenVNMR+SfIb8mMo1zTOiGHRbmMeZqjFEahbnKcNM1Y5o+QzVVIR5wNjCfDohsiTnjNbj40wHMiI26FViXGC5n7G1wjYZaySxVCznwDwUTNWiFFAy5EQKGVNXKAznk2MWjnYliFFS9wVlPC6fKcWiqcB3rLo11291DMcDH34jUXcNU/JsvgBSt6RYiEyE1GDrkaqWzOeKzEDTWNCREBxxOSGkwagKwQjS48aEtYkiR5qVJAWBshUpLiTxCiWekYtlGk7UncOFV1TnG4Reo8wdbjEIKZBE/OyQqmEcErI4Qs6IfaJbrdmfzrjjgOwCdV0zHOHJly3tKlLSlkhL8JaqguJqEAe6dcU4zpSyoW0UadkyDANFC2wLu80XcBGWZcaYPVoJ9neHT2k5uLj4ixNk5neuDtwsNU++V5T5Y4INAArorufv/E/+E57P3+L/+X/7T7i9/QNKKX/iMT/y+RcXn1ECeHuqXytNKMrHFtmfewK+vj4jgL/55ww2Hr+doM6Kv/qw4ytDxz+73vN+Oz0uKZc15Cf2BicaHbXuHjuH5IWUH08wBJJaJQKKxR0IocHoiAuGZda408ymEki7xXaGJYzEoNDrj9htE2mMaA/CnrF1pgwRWkkKgaSeUFtLij22dQQzUEaL8wKbEttdjXxL49wJlTOrZiQqRW0Vyxxp6xs0EWUjcvJcPdG4cY3MA7VWuAxLWFCM1KZlYMDKhCwzbjAYuVByxfVKolWPC5m2axHZYWxHFh6RPFLXKGsQIlE1iVAGSswU1xDEiUrNSAlusqimJ4yW4Asaybq74bwccMsLrJTktKe2oM3Mul0juyvmExwfAtAwlw3KCXRViB7C8FiEr3UPWdGtDFqfoSiqSrJuV8x5oqtOSArzBIWEJCBQxKkhzhNtbTArSU4FQUJViqwciMCu7RmuZ7Sdaeo1wz6QU0TbjFwJVm1FipLxNJFKRhlBcANW79g+3XE+7ClFU4i4KZNLZJlfoXOPsRW7p2+hrCDGEYVke7MDKehvOnQyhGWEZiHnQGMhzA0Tkto0hBRwcUbT0tcbojIQPNk/zrwgJNALMT12zFJyy+ADcRxJMVH8Fltt8WGPNg1wppjENEp0U6GlwBOxjWE4eFSVccuM0i0yV1R1wtYJaQVxzBQNKayQjcQtE2H/hNQK9i9u2X2x4NxAyQWlt5QMenxOOL8PdqBIQwwL/Vrhy4fkcM3x1cx5+C5V75FyJi0bGtP8Wf9cLz6DCvDEVTxdKl7WPxstjGeV+Mb6zJUzqCJ/fLDxPVJin77Db73z13n2hV/lP/tf/A+4vf8GlI/vTOqK3dWXEEIigPP5FdP0cNkoXHymVUly5avXeuykIrP+/NV5/TBFPDaagE8m2IDHdeTKW/7dF0/5TjfyL672nMxl9sZP6rUDDaM6wuKpqo6makCMWAksHc57XPQYwOQAWXGezgwvKpSG815gbcZWBTZ3qOB5unuHaA8Ip0hJIoXAl5FuYxldImVBTPdYwGeJyTU697jiqHSNT4pu9TZjekAbCzKx7Z8i6sg0nxGmUOaRZp0wVc90XmhQ+CiJi6PvWobpluBr2k2FkIZ+/ZS2W1Hi4+C2xQWETlTtM5QPzC4xTgsxFqwuKCVIdFxdXXF2rx6L08eAUi0+BNwyIqwkMbB7ssb5SFoCQ3igr97CrBdKLNz0He+9AhETUjXkDKFoXj0UbqQh8UBlO8bzHpkDRhXqZoNpE2JYofOMFBVJzUhpQCsao/H+cWr4pr/CR8nxboDQ4qMhLUAGqRyyttAItLWExWGMIGZNpWpimYhYdC2JybFMMynBEgJWG2wlyDkznE9UVcWyeEos5KI4HR5o+oabZzdIqZiOC/N5IKUKXW+xtaDpJMosUCQ+TOhW8qTeUIQi+IF5MQgsIq4RKrAMitKMaJuZpoG6LdSskSYTUkCzI/oFZCJxRNURqwrTNCGVxYUMPiGUoKpqdDOQi8LayLCPlKBQNmDryDTMtGuJ7SSUTL/KTEvBmIr2qnD/gaeylra/5uHuQ2S0oCL9usc5T7OrCPqIzYLm2hFdR14ySX6E1BarnxHLgXlOCFVQ4hlLPCEqy/TyCYO/Jy/fpr3SLLOgqZ7SNe9wcN/9tNaDi79Az5aat+fmT9yA33jDxtufmUCjAH+4OmOz5Nf2G5qkXi/YoIAoPP/ir/Hf+4/+d/wf/tP/ESHM/Nv/3v+YZnXFl772NxFKIYCPvvt7/O//1/8h9/ffumwULj6zrnxFG18vbfZkAvHzWAj+I3wv2FBF8DfurlGfwD/0x9kbgq8NPW/NDb+3O/L19YlwKRZ/Y68daGw3v8yR99CxJreOWtfkueFuf0DqwrBE6kWRdEaZhWksjIujbqDOYK0nqYVGaXxJHO4DIcNm1RJqyTJ7mmohoyhFUVcCqS3eK3TVEKNGS0O9WpCVwfbPqBsFp0ThjBEdyq6Zh5GYZopvsJ3GLwU3QNvfQKhQ1YHuWjD6RMmWelUxuxP9+hcI5Y79w5leG/x8ftz4lxXHw4SbFup2xXmYyOPMMcysdg1ZTLz8zgPdVmFbSZGJICGWmdV6g1lfMx/uiEtNzoqcDrTVGlRkWgKlFE7zTFVViLigW4FIEqvgNDv2h/8fyVuur68o/RXj8Z5mDeNJ4gdHSiealUeqI0pVFCVZ3AHCCoWkajqW88gSBbKssGbFeB6oNopiM8FplBqozIYYElJUaGkZ50hKR6QOhLDQNDdYaclBYtUJtTPkaFhOgloprp7sKCnTbztCyFhbM08DpquYhxdEr5Ba0l9L2usKbSqcO6GrTCj3+JBozJaSMjnBaX/HcPBsvpiIs4KpRTQR22XmceH8QtNuW1IlyGIhxTNaGshHigSpBEr6j4f5ATowDjUlZkSxwPnx6wF0dc8SRrx/B8KK1XVmmA6UnKhbGE41+2Oi0onJFWTJRA39k5Hib0lzS23XyG4EramrAzFWbNbPWaYzpl2Y9iOaFf3mmmVckMZzOu5pu5pSCtOxpgRLVRtuv6Pp2i+hm5ay8chyzelwQImZcf8BWtpPbUG4+Mv12B3+Z2eDABBk4V/uDrzfTvz2w44vju1jgfiPuZgLBMkHvvuN/4rvfOO/5P7uW6Qc+Sf/xf+GX/9r/x28m3jrS7+Okpp/+U//jwzn27+4/6GLi09agadL9Vr72+8Vgv9srSKPwca/3pywSfFb++0nEmzA41rTJc3furviK0PLP35yx96GS7DxBl470Lh9/31ynoj6iHeQ05qFRDIW5UDEiKw8aEtJCT8Z1k81q62CuCEr6JuIFG8xxz2iHDGqRSZLJe9RpmNZDKqskHokRUEtNygTyOXE4Be62pL9mbp5TogjabrCNteMkyOHhcPxSJ4tq2sBTIiwIaZI5CXhuMNHR9ca0rxlHDOrjWUqB6QqHO4eEPXIfIZu3SLFmlKdSKNnmmakMvRtAFGz8Dituuks41zoryS2tkjlEMYgtKCxazZXDaNbyEiyMiSxULWOkF4i4zX7jw5Uds1mp5Fqw+AzchGcRokJUNWRmCZCXPPed15wuvNoqXFTTQgH3nr7BmUbCA05RVxYCE5ijWYWI1ImipyRpiUBylTUlQZlgYRSCt1ofC6kYLA2kIonJSCcCUug6WrqypDmgZwVwtYkmRFmJqaRtrt+nGo9TWQkcsksw0yMJ5CJFQGRM20jQBRyjpSQcIPC4ZiHAW08Rl6RCihbGNIe3Uq21z2VGXFKU9kaqZfH2oj8HGuAGChhRtiESJLgAt4rZIloXR6noYsFoXeUKHDKM50y1TpgrKCpBUVbcl5QckvXB9zR4pcFW0eS75mPPWGJyLiiv7bM9ycqW0hpIi4gqBj9C7ZXP0eaK4pMLPOIMRqZM9lH9vczsMa7iuAX6vZt6nWDdx8xnCNaaZQpKHFPyQrZJF69+g51A7rxpPmBmM9Er2i2BTd+jruJ/Ax5WS982Mz8UjB/dBP/vW7m3X76y35pf+EKcF95/sHzV3z13PPX73c0P6S7jgCiyLx6+Qf8F//X/4zf/53/EyHOfK8w45t/+P/im3/4/37sJLN5CyEkh8P7fJxXdXHxmSSBt+bmtdu4fu4LwX+ELOB3r/YI4Dc/wWADHgOOZ0vN33v1lP/H85eM+pJK9bpeO9AIrmYZB7qNJuGYteB8HNn2hVVnabua82lCREXV9uxuFOv1mqv1FfvDjBQblvwBan5A5wxGMY+OiMGSkGKGIJGpRtUzQUX8MmKtIVNjs8FPCSklMSoQluhnhFMEtwCWkgPtqqB5QjQCHx2gSdGw6Vva7ReZ3PsEH1ivWqTxqBFICecfCGdJ8pG9ziyHI/2qJhcD8kxtJIfDgfEssZUEU+HmAkngnaeuDf3NBkgI61Eqk1RNkCO1bBjP96So0FtLCoEQ72n7hna75/igMLnC1AGCRVWC5XxC24acniOEwJWZq7c7kp+x1Yqm3bI/j6SlZXvtWApoUyFLwNgaW3X4NCCzoWRPiIGUK2Q5kkRGCYOPEyIHmmZN3fZMwz0CRSmCuq5IlSXmBS0zKVt265aiAmXuQChUnTgeRuLksbpnGRYUhqoxyBiIKZFzoWSLKBXaVkgDUixEG7B0BBKtWmNtQ+BEzDMhR7p2QxIHpmXGVJJIj0qCkDKpeNbbK7I+EFImx0BeJLpLmOIpWKYxsAyZ6+eKaXRARdf2vP32jpd3H2GsRdYwHxsEDTHtST5ga4+qCmYtyXMghCPLVKHKkYf3NaLukNJgu8hwMKy7niASzu05nY/kIGg3W6zkcZoxAT/0VE0Au2faO4rYsRw6ZLVBpAM5CeK5QfaGzfaawoG3v9Ix+VekEKCFXno2T3rm0SHUnz8H9eKnU5AZ/3kbsvUGoih8Y33GZMnfuLv6ow47TiXOJvJuN/Fh94LTf/rfZ16Oj0/6Uxf7QimF4/GDP/6ry4bg4jOsiYqdN6/12CQKB/szUAj+I2QBv3O1Bz6dYOPaWf6t2xv+wbNX+M/hYNVPw+sHGv5I8gLZeMIYCMcjvb6ias6czgrnHgeRaSUwuUL2jpIP7E93UG5I0dG2LetW8vJ4wk0SWw2kdGB56NFmjaog6wPLpNCtR+p7cnoHJUFQyEWg9RUkwTwvtI0mUdisN4zDSFWvWHVnosx06W0mdU+YFmTXICrBdP6IrAMiK6R4IE17GrnC+44iPdNhpG83VG1DrSPL6YwRlr41VLZnHg+89cUbxvkelMCfPH2rGVXF9dUVyRh8OOPHka65ZlhO2HrLVO7IUiJkoa5uSPEVT56tkKJDrz/kdNTsp3ueyA1qq5FDQOqeftty2geqviBE+HigXw/5iLIWnGOz1bgcEBm6G4MLE1a3yDRQkSkqUQo0tSD7CLGlsQIhAjJXOB/wIaH8kUImRoHMBd08o1dQxJFlTtQri4sBouL6+Y7APcuoWasG14CbZtqVIgSHrSqkM1xtOlIRFC9RUiClBDJNsyEdJ9wyoepIyA5lDKIUMpESF+bFY2oDJXLaz0gCphIgwRiL9w+kUDBVwc+eEgU6GKq6ohQLMmAsJDTBR0rSbLfPKWFBk4hJUMtCSSPnSWHbDr/MNNWM1Io0rfFLQinDzXPNfDBIHLJeI6v3yQV0VROko2u+zOH+A/x5odm0iJxJZWKZA7rsEGpG2hnkQrdb49kzziOVNfh4ROqMsI55mTg8KLQtjOdb2utEzoYcNKbusW1LHj/EtpfJ4J8HdVLU6dKq+AcV4Ovr0+MgrgJHGzhYz8EE4vdmZ7j5xwcPl8Di4nPkxlVUr7lWLCoz/Qy0yP5xsoDf2e2pk+SXTmvkJxxsfGFq+Ov3V/yXT+54jW7DP/NefzJ4nGifPseNR2TOdBtBbyKIGiFHUJFV9RRpIqb1BKcRzRfIbgQZKWFkDoXpNhGp0FIhTIeRFVJblErIOmJUZtw7SC1aLKA9OWaEOqJzz3SUVI3ELSesUlRtxo098yRYtZrz0qKMQ9eFK/0b3Md/Q2VnFr0Q3YlwhOQX0C1iAdl9AS1ecnVzhSp7SIocMstBIVNF1fXMM6QCRqy4e3WgkhqtoUTH8TRTmZrD/YlWWE4Px8dgppyZ5zvmrDE60ZiMWkn8NCESzP4eU418+HXoupnVbmC6a/B3AT6uR5nPR0gdwtd0dSQZaNWW4TzjJslme0V/VTHNEj9EptNE8hbTVbhUsBKMqZliQJUVpEJMI1rVZDEjlUSrGluvWZYBP0VyWWh7C+FEkRWpCFADdfcFxuMt5B6XT7hpJhWJMgZ0wdaF6SRQWiIMxLAwjwVR9ONxr9IMw4iUilfv3aOMIsWAdopSziidkaIQY8KqCiiEvcDlmvkk6HaWOEnafktJAqk9ShpSmJCiRjaS6CXaZHIZULJDrw0lW1pzIPiZ8/k9cg4k4aibxLDXLB6MGQiDfXytTYWyM9G35OjIcWF4CGxuLCnWSJGwjSCzYFXH8TiynF9R1S3bJz26aZj3jpwk7ZMTy7ihqtZI6UC1hHzHNGWMFaw2G+SqYzlNpCoQI0T1QCkJs5vxwdJ2DUsOCJWJ58A89KT0ene2Ln46yQJd0vz9j57yxFU/c7nUryPIwj/fPd6V/P49giyPm6426j/xvglgUYm72lHg8zkR+eJnU4Hnc/3aW+WzDj/Tp6LfkyX8k5sHBIJfPK0+0WADoL3cJHptb1AMvsWlE8fDmSc3GiksVWMQ0hLyzM3qLUrsEdUdMSWUeBtSTSUrQrklmDUp3pKyROVCVjOEjC+ZtmroWs2cBVrWtM2Z6XBG28eBeZoaLZ+S5EjdCTKFflMhk8LaDUUEetmx+JHKalI5sD+8JO7Gx/SpuCWGiZI2COtobY1bEl4trMR7bLuf57CMyBjQUjCf7jC6Q9mG83KgxIQ0MBxnUoE5Caq64IOjazeEKWEaT5GJVe9ZokVVt/SqIosVw2Hg+qnH54lw3lDUjE6/ijvNzIcXuEPArrY0dUPVNbz/nQeUran6GlEfcd6yzAmMQDaBTbtGXHdUdUuYPcTHourFJZTqyanQNTswgoePTlRVZoyvMGZNiIHiT6RsadWGzJn9q5GuqbH1jNHPkaLHhwOLc8gWiirMy5mkMgXQ8gTtkTx11J0hFI/qDe1aM+wFQhaQj89TUjMPM6pEvAvEKLGVIsVC3fSPKV0xkV3Bx4CSNQFFv94g1jVyHOieROqVYToH+mYHVWI4fUSKnpQNWTgSnugaRKVo+kJ2GnLBLwWlW4qGeRjRsmLVr3DuxP2LjBGgV5Jl0dRVQ6UtxIStrnDzi8dgI9TU1Y778V1EEdR9S47gXKBZ1aRlIIWOqjPkYrD9GalOKGGpNyNEg5ArpjGRU0dlF2Ko0EmQyoQSFTFZhCwoIcgesq6pVwvEjtW2QYqEPxW8fUHJl4vIZ5UEfuW45tcOG7qoEX9G0fPPNPGnP/3a0PNfe3WDyX86fTDIzKgjTmW+24980Myf7+nIFz8TVBE8W16vPqPwmDZ1uUI8irLw/7m5B/hEg40sCv96fbrcJHpNrx1osBR05VjvBLlojI4sPjAuBWskSSZcfMmqC/gZRJqQ2pGEYWWec3//bZLQ5FQ4v5qI5zVy59GVormGoBbqpuN4vCMsmhJWzB5q20A1IOQWBGhT8HNCsKLbFobzgSVYZARpJFpWpFyQUnDYnyFLkjghFXhXCFkSs6HkTAKCiMzplnlaWGJmW1XkdIdtO4If0LYQs2Y4ZiiS9WpNjAvTEFmvd+Sc2T3Z0u8kWcBZTDTXW7ROhEUj5Ui3mRjOkhBqpvMDT58+pcQaN5zZ3fQc7xV929Lvanr5lPQVxRLu2DyxLKOihDUhDJQimKZ7YpNpZORhP1LrCBJE9JSwYlkK0zwxN4GqaSlOIPuR4BZiaghecRwLMgdSdaSqK9ZaYWtBUU/IS0cQkVIiU4ioYYKS8elInhKiUixhwZo1yAlYUTWGefJUqgU5I4RHVxUxFColuHq6JnqPXgxunHE+IZVgHmai89i24fAqIExid7Xj+fPnCATTdEabSFNbKBX1SjKnA2lYKLoiTCNCWJQVlAhCSkoaH3//9BHvJqSS1N0zzmePtYJlDJxlRXJXyJS5flsx+3vqZ554zkQ/ErMlDbc0zY5hzEgVSe6BplaYJpGOPVPyRO9ormucz8jkUNYTl4WuXeOyoShYdTXzLHj69Esc7265uzshlEZKxWk8YnThavec2/0Lmuotjq/usY2n6RKb9he5P/4r5tuObj1zfLC0TUdcPXxa68HFp+ztqeG3H3Z/nAbxA9c9nQUmy8sdyR9iHQx/7e7qj4OMH3jvTJZs/WNHtmdzzawjv7898geb8+OE34uLz6A+ajbh9bdq95W73Lz4Pt8LNmQR/MK5f+2C+h+lUPioXnjRLJf3+TW9/sC+dgIHtb0iRQVyQaiIVgVrYRjfp642RB/IEa7XijMSxEuCOCFNg5ITx1vBcKfp15J2JamaFTmfmV1mbTscihTu6XqLzxljABFwS0Q3gkptsHXFUhz74z1KWmK5xXuLnTpksLjckHzA+cRuVVHqmWk6IuQOGBiXhsoYRM4QO0YJboESGoIqNP1TlNJMx0ByEjfNWCPQSnC6m8gistnsCNFhaoswGV8e51aYescynVDFYMwRKk3bSe5ejNxc/1WE/hZoQ1omwiRRfsWmfoI7nylqwtf/hmwcFYZxzAwnAXmPlECZkEKxuoLT6T2Sf4ILkhIFle2JY0ZoTzInMluEKGDPpNQgtWU6eqQdsMqQfcLngK1HMJZFCOR8TchHZA5U7YqSj6SlQTITJ0HOFUYWmnaLJD5209ILMTtMrZE5YlYJETWVFHiZGA4n+maNsRaihywpIaKNIdqCKJYYEkIqVv2KnAqn/QOH/T3HjzxPvtKjZEGakZAX5nQGUWGMRGhLcRlVb1BWU4qDKlIKhHkkTSv67RXx3hHnRL1tkVqxzAUhHF2vicmgrEFXGpUbqmrP4gXnZQBhqGVF/faG9grM5BmHDDKRoyImwemjgrEN2rrHIY9Vh9Yd03CPtppxXgjzgXGEurE8fesdXr34iKJmpIho03F/fJ/NbsN5P7F7rvFhQtpb4vSL5ONXWfxLatNjTINqBubxsmn6LNp5y9+6vf6RQUYBvji1/Opxze9tj5f0nx8wq8R73cQvH9c//AHf936WAk3U/I27a5Io/Kvt6S/mRV5cfJIKPFmqH3qC98NkHmuaLv6kKAv/5OaeKku+NLZ/rmAjC/hXl/X5jbx2oCHkBkQgO0h5RMiOKCNKrzB6xupMs3aUrFm1kTnPON9R2w5rJVdX76DNA5qOdSNpGk2/a3g5fZ0SG0oEvyT86UBtFMYsWGNQBGZXU9JMXBIuNASZEGIheUvKGue3VFoRXKbMAqkL5ERbbXl4mMiD4uorNyiZcWwwlWS70tx+dGRcDiipkbnlfDojhi2rqy0mKtJ8RKkdVi+c7wNd1dDtOh7uH3AqorSgqizHFwvXX9ggVAAv6RrBeXLkGSqx47C/p7Vvk6eZtlzTWItsIFcWoY+U8j6NtSRtiEugeMM4eZTJjEdQAdabFVHANAlezD3Ba1q7RTeK8+kOszYsaWTeZzZXGmkjC/fYCpYl48NETgETr8lKUPLCco7Y2uKzpHAm55e0zRVStpzPL4kl0Nc9uv4KMS5EHFWpMCRCAB8dQRSquoMYySiMlbjgkLpQpoCwPQ/nAyKAKApdadquB1EQRSJlIKUIRTKdAkooTv4EUrH7ckNRIzEIlBT4lFnmhLUBHx269qA1Vkp8gr5bsx8OWK0RokJXNcNpQUhHd9VQ1ZYlLkiTkf2MGxWintDCEpeFxoKfG0wLW1uQwtNV1zTVivP5BMWzWjcMB0nfJ5bK4AeBrSyyVrR14XAcIU6oUqNlyzB4yGfm8z0hS9p1YbPtKLqQowcR0f3CPGt0lan6t0jTATdJlPmIcUg8/XKLqN/HNM+JZUNF92muCRefgjYp/sbd1R/dcf9R1zlRBL/5sOVkAt/pRvLljtkf8TJzVzvE8XFD9WNrwT/+oiiPAZ69nBJdfEa9NTev/VivMmd9CTR+GCcz//DpLX/31ZM/V7Dxol746HKa8UZeO9CYB/VYTMyMnyEkh85gzR3JWXSbOB8TXdOCWTNNd8RwwI8tYfsM6sJ4Hkmup9k5Qn6P25OnJPCpYFVhHCdUSWgVSUtLhSEKKJOi7xJLXpg5kWOkrmqW4IjzjEIg6l9AiFvcpNGbiqavWfUrmquCTgZjC8vZgR8QznBMM9BSlCREQZgClbnGNA37lw/MQ8JNgm7lGcZAcB6ZBNIoatMxnBzFF9q6oa4kD98+kqqARtBed4Q5ATVeK1bVc1Ls8BqiPRPyTFtvaNvMEF/S9gmpHcukEaaQZ0kRlhJ39G3g9DCy/8iTcsXVbsM8TAznidwb+tzTr58zDJ7hoMgRZtsyDIJWS1SdKXKh6no60zKMd4iS0FqhawE5IZQhp4ZSWdwUsWuPlR0qLxwPd1SVpG9XhKUQxMhaP8WFe2JosWYhLQPTkGm0RJiMaB3uKEiiQneJ6CQpBLyXzEtAJgdCYRtL9AWkJcWA0QohGnbPrxjuDkzxFmEEvgyIqJBpoZcVqn081RBK4mPG+YQre/yhsAyGamVoNy1nvzAcPU9vHgO48xiQxqBriSwV1U4/du/KD0zOU4KhXTtm16JkTSkRSeR0v+d4f2B9bQipJqcBIRIxRGSyIPbkpIihQStPsYV11XD2Z6zQRFlY5oFmM+PmLaY8YTw/0K8NysBxv2Y4wuZJg5scMTpImqO7Ra8rYtwi3YZUEt3mfU7Hn+2OIp81Jgt+c7/li2P7+Bc/6gIlgPKYAvR3Xz7hnVXDP7veM6n0F/VSf+odTeBkAqvweg0RCvDzpxV/sDlzW/1sTFu/+PwwWfJkqV57UzypyHJpufrDiT8ONv7eyyd8cfrJgg3JJcZ4U68daBg9IWzD5BQpn8A1YK5ZhpFmLSmLw6oVImSU1ih5RV0tmLaQysC0P1I1DrW+I6SCMpBcR20kopoYDpnKnmlUi1CBSq5xSZGjp+oglAVCB6VGisjdyyNKJbwbWesvEcZ7WtNRP6uQ85pF/UtS9txcP2U4nRBqQ5KRmB+7G0nh2WyekJZM8AljawY3olODqgxiSlhrKSVRiqPuGrQuDNNI37bYRrJ5a4eoJdOdR6lEPGe8LDRbhVwsMVkikVgkor8nuRO2F0xzwaeC1u/jxw5VLMYWur4hzkcWVZPHSJgi3jmUqjgPgZIHlBQM0wlRYC4LJSnUuCCpeetLK25f3JGcQApDvWrAzKRsiEumcKCyDXYdkSwIk7FyTZSRaYQ8L8haI4wku4gLCa03FGXwBZpKsPjM/f475GBIS0PMBl8GlBI0KiFij5siyzBirCanRH+tSKLB3SdOd56qaxBJEZ1nniMlSYSSXP/cGiUFr969ZZpeotpEiYp2rQjZo4rCrCXKFOZloVINSkdCmigugimsVjWqSpwPDqEFu7csWlXEJdKsCgTw4UQJK6ahUNdrlHQgBIvLzGONaRLVyjMcA/N9QCZF1UAKCikcFENUhapOxFKwbYtSE345c/30KcOQOI5n6jZjO8l0aAluYjoqlIBmN7GcWuSU2ewMqmSaKhHdESm+CzkznjXLyaN1TWUiyRWunlRIveerv/wbn+aacPEJq7Pia6f+8ZM/6wr18dd1lvzCccWoE7+7O1yO6T/2sln4oJ355aPhjyoxL1f9i8+jAl8eWzavGVQDHGy4rBU/zsfBxj96esd/48Wzj6etv9kC8myu+YXzin+9Pl3Wntf0+oFGnYlqZrw/IR30rSDJ6XE4nY5kd8P1W79Mu6sZ/Xuo8IDIgjRFVJXZbhJFrHC+0PU9UxgoJYCu8S4jVYXPFZXJWGs4jxFSpq4sJc9Mc0TkHqPWrDegK00KmlUT0U1HyXdUTcWwvAuhYrN5yjQGXr3/bcZhQvMRfhGY9cBwr7nZ/hrbJ29xv3+Pda358DsniJpZBFLOJJkpIhG8Roqaru3JIlH8Qg7Qr69oVMs0zCyTZ9VXSB1p6x5mRYqS4/HMs23PskT6SiCTJMWXxLCmJAdVzWo74ZceE56S7yzFPqcsH9BfnSnPBmRJ7F92PFnXUAxCzzTFkGaNKpLjMVDbjt3NlnZdsZ0dwXuKAh8TJWqcHyg+4kZJv5L4OWKrQr2b8dni00Sl1sTKUfKKlApSe9aVfRxKKCKiLOQQkASSCFTtmiVNnPaKki27G4sTC2EeyCKxfrLDyEKJE0JcIYqBRXBlFeeHhRgEsihKzti+ou07lvNCqjVRLHRbg1DqMQjsV6RlxJqMkIVYWvwy488eMWhyJWmvt+j6TA7gs8U28bFNMJacHNJY/FxIyZJlpust1kJ2Z+YhEVODMsvjyVXdMw0LyxIxsmCtxc0KIR1NZXAnR5SBmBeUrGnaipQjjYj48UxeIihJdJJlCZSo0VWHMumxzfNUqJornJ+YTgptapx/n93VjvuPJFls0bkn+BM5w3yKmM4zPHwDHyP+NMP/8NNcFi4+KQL4+VP/2jnW8Lh/lsC73cQ3V8Nl4/B9CvBPbx5wKvGb+y2iiD+aqn5x8XlSJ/n4O/6ajy+Ux4ngFz+eeKz3+gfPXvH3Xz5942BD8nhC/UEzcbKX7ILX8dqBxtlldCmsWg0ri6jgfDexbhp8mmjUNeNxT0lPSVpDEaRcQeyIWZGTwwDCFAY/0LUN8/mx1kPXBqU0IWSK1OTsIRU6vaVsT0wPJ3J2pPKKolqO40KMiiQyxMxGj8RS2D+8hKzo7MLxRUOSZ/qVpln35Aj9VlJ8T7s7ETmzvx1IaeL2JNFKUYSmZIWQgaubmmn0zGdHcJllHKkbyfqm53yaefnRB+yrxyLiqm94/70XIAq6vWe7XYMK3Px8Idf3rHcbmlYzndaIrsYU8H5GcU0cZ7KXHMuHtP1XKMeMmwSbZ9eo6hXdasL2mTRa5gfBMktK7khe0FQNfTPRtBVRDOxfnpnHBTdG6qaC6kQsBaMLLlakANMY6VuBth2VeMLs96RUSMERUqHpB6IviNKSeofpwMgVwzEhykyhY54CqgsItbB9foUUHe2m5vzwgiQkbdVjVCZng207cowc7wJN01NMwk8aITPaGKruceBgUQ6pIJeZpm3IOQCGbt0ynU4obZi9R0fxOOuCTCRjdgWrarp2xeHkyR9/r5IDfilUJqCMQEhB1Wi8DygFRkZCSOiqoaXGhSNRXdPXexaXIMXH0wStKWS6XaAVnhAkthbkaqFSgspWhABu1lS9pK0cws4c79Zoa8ghkcRI23WEIulXWw7He6TwtOtnBBdRKlNVPUscSOKGMhlkHmmbSBSe7loQwnNy6SjyfXI+fnorwsUnSiB4NtfoIsiv2clWlMd6hO/2E0dzybf+QU5mfn97RCD45eMam354F6rvkcCv7Tf8o2e3xEvQdvFZUOCXT2u2wbzRJngw8RJ1vw4Bg478g2ev+HdePM4zepP3uYuK337Y8Q+f3V7q6F7DawcaOURELWnaREiCkjU+ZZSVaH1NjALDidnNkEZKaimzgQqUOOFmSa4l4xDZ9DvuxhNGVeQ8I70hKIctK2R2uGOmqjqk2TAcTmA8RlW4hwWh38VWPQWBCAbT1fj8FFkSdXOPiLCkW6pVTfK3DGeJc4Wu+0Vaa0jqAwiW0X1IcA2UlhBOhKAwbCnJo5GEBbyDkDx117Le9ohUUNTUppB0QAsNEaIv9Nc9tvI02wrRLfSrDkxCigbMER8sqVX06glJ3TPtI2d/Znf9VYp5SY6R+/F9bI7o7oAbOpRrmAeBKlcoJahMgw4WszHk0WHVmqo1GF3I5kS36VjmNUJP4A1KdCx5JHtYX0PKjuAVIjX06y2lnBGyJsWCyBX1amK1Uuh6S3EjSWzwyxlpE6oKUHpUkuy2NygZqJovIGxDWAR+PmKMQSVNW3XEvGC0htQgERQx4MUJIRTNBpoiiW5B2R7jCpUtxBioZIdpLdmB8xFtC/4I2XuKFAgsyiqKmDAWhBYYazk+3FLkTCwFVSrqztBWgiIrXD5QSoUaNVY+zt2YxkAxGeGgWsG67sEUpr3EFIE0Ft0ochCkqIhLZMAzR0nT1riUKaKihMR0mqnaFTEsTDEiTIeLgdpGkAlbN/Q3mruXO07uAWkEwghynGiba6Zhjw+FvBhk80CMCzleYRvFuiuY6p5SKmJcIcqKZn25i/JZocpjsPHad90/3gff1o5vrYZP8ZV9ti0q80+vH3jRLPz9F0+wSVHKHxeB/xHxWGD/fK55Ptd82MyXjcHFT71NMPzKcf3GaT2Xhgdv4HvBxvNX/Dc/fM7mDYI6geDnxp53h4lv9+MluPszvHagsV41COVJUrDqFN4PPH3SodSEUC2brSfGgkga02X8MZGKwxpHyI62+SLFBvLoGIeXmCph2oY4BCgrUDNJw3I06MpSZMbHD9DqhM8WJVq6XYMxhbhodK7IZgEipjSUtCeVhBCCWr6DO2Zunv0qifc53Bem/RHmDcd9S1ULpkMmTwkpE1HUlEEh1xopC7qp8bJQIWmqjhgl8z6wjB5pHTEnUkjUvcC2FefxwM2zjvWTilG+pAjBIh5w55amW3DTGUtLlh0v9t9BRgW54nQ3YcqB6klFKBP12rH/ULBeXWFqDRzR6powt7RWk/tMwHH15IbBviTORzZPDPMEMjZEL7AmMs8SrRNuEcQlEOYK80ShqxVKRvquxdqRXApJFgyFYgO2bsl4YgqEbEhB4WMghTOb6y9xeLhH5kLOnhAllIh2M9MSCWFGaUPKCy4IrFCokhimQKss11dbhtNEShFbC3yMiGiwyjJNE9M+PU4VbzJ1p1EqcQ4j3FlK0fgUqKxF1xaZJUZuGE8HhBKolYciUFogiqbuN4TljDKSum8Rw0QqBpET9cahasMyFULyiFSQ05ZYQZj2uNCyWoErHndKSOOIeUCrnig6rq+vyPNMDFfkcGKIB2ynqVpJSS2inBnPiVUfMe2WZXAYlfFhj8jPKWpFjBOIRI6CMN+SSqSuG0QxOH9CVIq2e4qwAlMiq9qyL3vCPOC9ReTLXdnPiq+dep7P9Rs9J8jMv7jeX+6+/xkK8EEz8Q+e3/K3bq/YeMuPiui6qPl3P3rGN9Znfm93ZNCXYP3ip5Mo8Bv7Dc0bTp4uPK4dF29AwFlHfufqwN99+YQ3ecdlgd9+2PGyWZj0pWHHj/P6Xadypm0mSjQ4PDkntk8KxJ6QIlqOZDJzOROPPTpbVFtQokHbBqWumOKezSqgasV4EExDxD2saDcrTLdBLBLaOyZ/j82CWgZ0ZRHZE2TBxAotG5qtYZkGbFMh3ZrD8HVinrGlxaiKj15kZDCk2JLcE9w8Y7cLtjYEUWhUR7tqMTtgmTmfFHKzwtaaEgtKZNwYIFVgBCU5htFRgLw4tts1uTzeuT8eztg2oRrHlCQuzKSciT5T5ozwmclFzn5GqYTpE0ULGrWme+cpQgaEuMM0kuifc33zwKqrce5MKRDTjK5HhlNPkhrTBXKZiFJBs5Cwj3MXSmE5z3T9Mxzvo3WhsV9BSkXba9pVTZKZ2S+M7sSSEvMxMB0jttkBATELYuPZbQWLOyPzgbZVhGg4HT5CFItQghRndLPG+QkvQJTHupDkVuRksXrFnM6kNJN9y4MX1CLQNJZUWkKeyE5iKsEyjSADiUAJDVMZyQSEkDR1S1oWFheIBYqPxJAwlaSoheCgNpI4FFAWY9Rj2l3UeA/n80TvLM1qh0gLpQhSlCyHE0iJ1hVuSOg4MR8SijWyiixnj4+JLEeYBbrtmeNI0xiWsyO4I7FYqvZxV+OHHhEDsh7xY4WPiW6XiOFErRtW28DpviG4EyVkhrMnuxXajmilqKym7Z4gcGQ6nA8Ic8SyAuM5LxmRNsQw4V2ib+yntR5cfEJEgaeu5ufPK+TrTv8uf/zHcuk09VqygPfaCX0t+Dsvb7D5h5xsfF83r189bHhnavgXVwe+24+XYO7ip0uBZ0vNV4c3HyyXRGGRl3XjjQn4Tjfy1bZ7o05UAsE2GH5jv+Wf3NxTLqcaP9LrF4OrhqY+s4yBYmpU2pJdpHhHJrDkgleReQlEcYXRI63YMi+PrU1tdaZfD0xLRMYaZQ3BLfTXGtNOTOOJsmwQ+ojUM0ZVCL3FTSNCtESZKVPAVZG3+q8Rw5EiC8fhjqYdqboNfqrws0Z3t4SxR1UN9/sXrOw1qmwoUbBbrdltnvHeh6+I+zOrdk2MD5TiOR/2SF1xOwXW6w1VLxHK4o8L2miaxlLXDbE4jrczVlmaXlH3N9SVIeDxi8ZWBZlXpKZgbU1VB3IMZCRRFWoTMM2EiyekkEiRcXPDaT9i8kzYPnZh8qGw226YJ0fKkEJ8rC05N9iqxpot8zRgdgvzSYGU+LRH64BUHXpzplMt7Urz4f03Eamh5ELb1yAU09mxedahdIUUPVp70BuGcySmFU3lQS2UuMdWPUZJpqPD1A1uCphKoJRBFkv0Az4amrWEuCBY8IunbWBeHKK7wdSPw/ncKaKsJc0SRGa13aDsmewVSiiGw4xRiuUcyUDwgXazom4FKWb8HIlJ0vQdVS8pJeKnSCygpebs7zC1psQEArwbHz8GhcyadvOEqrliHPfklDnMR8CjvCRNnqQmNtuG4eyJCWTOaJsopWZyE6I0rLcdix8I0aBNIecCeY0rdygDOYCxDSIXToeBebimagreDUzHhcq0FF9Do5lT5Pzud2hWnkRGYXFjYPEz23cWTPkK++MtUg6sribC0n56K8LFJ+LaW/7eiyes36BjzPd8Y3NmVJc77m/i3W7i//vkgd962NH/sCnKH28CSoGNt/y9l0/47tDyO9cHHi4FtBc/JVQR/JWHLfon2LVm8ZihcPHmkij8i6sDz5aaKr/+uYZA8EunFe9242W2xo/x2oFGYzMidVBG0iDIk0epgI8ZITLzkJC2IQdI0pNdRNgjS6mwKlL3ga7p0U1NcAGVHtuc5nTCTxUlZ3JJaLlDxzXL/YzpN6z7DSnt8cvAWezJ91fcim8hykyVW3Y3W5wbCcs92/bvcJdfIpVguznT1F/jrS++w037FWI2vPvu7zO88oibnjiPaCKn80RTSyq9Qu4qfA4cXxWqrkJJ2F6vaeuaeRwZjjPzPOBdYXu9wQXP9MHCb/6tX+J+fA/vQKsd/uxpaovUI+1OgNTMg2Q4OLbbFtmfWeZXmOoJMR7wBVy+5+GjwrZf028Cx8NCU1k+/O6Z1argyTSdICSL9wtFHpnPhskNtLZBK029sQQ3wWKY3Z5hTMR4ZjVIkmwoS2bVbiliYpwStiq0awVoQlREMUEUBGlAK3xO1GWFSAvTENltV+gqEnygFPNxGpRhHk7Y2mDFjDUGIojc0u2eMp5GuivoO41fFoo6I4Qhp4hziZwzKSqy8ihtWEYHSqAaTSqZNAuef+0tgnfIqFldGeblTF13pJJo1g2jeyA5QS6B85SQKpGCRKEJh4Xqac1piGjL46wObTl+cKTUCSEURdTI0qHqQr0yJCyxnFC1YbpN9HVG6w0pJygDur5iCbekbBA5ssQBq2tqKXBJkMWACD1UknEKSFMRxR1xaGj6ls21I8YjEgu6UILG2kQuNcl7EgvJzfzcLz5nWBaOh5f44rF1QWiLkG++eb34iyOBXz6u2QTzeEjxBhcfATzYxxPci9eXROHfrM8kCn/79gab5WMW1Q+899/7XBbBzw09z5aa398d+deb0+V04+IvV4GvjB1vzc1PNN8hiXL5Hf5JCbivHN9YDfzaG9bG6CL4aw9X/N/feoG/zDD5oV5/MnjwKHtDFhkVG3Y3X+BwvCOnPcZEZNZkvyMuoFqP6TKlCKy1NKrBSHg4jpzuPd0aoOCnTGOuKWYmF01OIPOIMQrbP2UKL0hzQ0KRvaWEayrtcItm1b1DHjQvv3VHdG9je0/zhZFOgry6Zv+e43olGY8tX//2H7Db7JBJUNuO83TEjwllFefhgKJiFkcgo0ykJMVpv0dLAzkzjQNLmMhTy2q7JseBUjLrqw31WmPWmoY1w90tVbvCdJbp6GmTYf/BEbc4yJrq2jOHCbnvQMIYvoFVb3PdfpVcjbR/9Rs8fBtkfo4y3yEWj+kk1TaTJs1pH6iUpbt5hY8KXa0pU8Vxv3D1tKPKNaUkcn2HmzTGLhi9kEqPFA2rpxWUSBaFLCaUqUg+EsIeZXqK9BRXPw5ALAnoUcGQgiSmwDicCMlha4N3J3TZEsJIKIUsC5JC3VbUpsVNC+fDjFICayTTtGdJDl0JmhtJwaLngs+KHBw6dUhhqHpJCgpbg206/BJYzo75MJAQ5NTjUsRPBWkE0mT8KFnOmVw0yIx3mqVE6o1F1oV8UnTakk2kqgs5LFRdg2oUzo30tQbhKaF5TPmzmWIi8ZR59sWvENItfimPp1crRRIjwxmMvMJWe7I+kVxFjGdSEbRdTZgSMs8Mw0xdrSF6UvDMo6KybyPleyyTxDQOEOQIrSkUIlKM1G2NTw6fBpLwnA+Cm64mxRFrN5/ScnDxSXh7avi5c//GQQY8pk2ZLL6X7XPxhr65Gtl8nM4gf9xdYfF4utFGzW/f74ii8I31+bJRu/hL86btbH9QkJkoLhvdn1QR8PvbA1+aWlZBv1EK1dOl4leOa353d7icavwQrx1oJBzHB4esnpND5uVHI42t0NrSbmHBkuSE7jUiQ11XGNMzLzV1KzF1YI6KzW5N1WQODw+AIZcBZMtqJXH2QM57JE8peo81MPuJth/pHjakWLh6eoWtLbX7BQZ5S7bf5PhwonPP2Ot36daaIgK27Xn5ag/WIxpHcCNGKUqVWJZ7zgfFhCXngggBVQ0UKUiTR1JBlthqRjtLCAtKVHTPtpCgv75mGE68s33GOA/Mk+Bbv/Mu10+vON8f+erVL0D7CiMGTvs7ZA3GZupaMcyJeDKgaqruxBgd88sT/nRGra+Res8YXjzOWkgSqS2+jpB62lVHpSdq/YzxNCGkpySHNIKX7w/MqyO22RGlocSGqq1JjNiqJsRESp5Cz7xP1May6d9hf5xJeWKZ3ke1FSrXlDRQd5aUEi4GKvkMZWdSPlCyYhwE2miWsKB8QiqLRJGWE6XuGWbPPHhy8RilmCNkOWGURMmepXiMUFRSYEOLy5bzcSYsI+ubmlwWUrD45BFKEMPAaRipm47RLcxnR91oul5xejmwLAvRZ5reMpwKUoqPf64SHwNh58iVp+lbYgkMJ48tCqFHRHkssBciE7Ijmh19VeNFouoErnxAytCsJS5kCBKXFwRXGLk8FomPinGe0EIjlUeIHbY/I1LCTYmSE0oLcmqxSjBPe5J/m0pr0jiR9Zl+E5EY/MOOZrUjxHvuj9+gUhA9rK4dykqEq7i7PX9qC8LFn48ssAoG+wZzM37Qb+63vN/Nl9a2P4EsCr+/O7IKhq+e+x9ZHA6Ppxvl49qNv317zRfHln9xveeucpcg7+Iv1sftbHf+zdrZfr8oyqWj2p/T9wak/luvbt7opyAQ/Nphw/vdxL31l2DjB7x+oCEDorsDOjbbLaIW5LHBUdGbFi8i5EzVaWRZsywDJvRoCn5ZiEtPiDVyTJhS0bfPqNVEoHA8J7Sc8TbTyh1KK7JoON212Dpj6AhN5tkGpM2Mg2ee3iP6GZWf0LUTOkeu306MU0bGt3Fnx3g689azJ6yebbh9713s3DMcArk0bNYSUEjVoKQgF0d0gmmCIhJKKp698wUOhz1CGCrTs6o7fHa4aaGpWt775ndJMlBevKC/aTif99w8v2Z2L8BPlEbR7VZE9YDFshwNedEoVcBJuid/hduX74E7QW2JWSKFZbXShOKJk8CImZAcyymyu7qi0jccbh9ALcSgufnChofbl0gfOU8glyNtXdNeZVLxiCyJMTAOgeI0yANFaFJM3N6/IOWEtT2yslgrCYuiqgXDOKANaNXiXYRsGWaLrQoJiIvC2oKPAXJBOoFWN8yjQIuJtl/jwh3zXGGkoKrWjOcDQngaaxFCE1wg+4WiBaZVBBc43ztUJZEi4sZIDBBzwtSGurWkkKm6GneaGR4cLiSESGyuOpSusTVUFkIsCCRVq9GrhE8BLTw+PIDaUfcNJXmcH5nnRL0SyOpECJpplFSdIWVDKS0yR9x8IpNwOLSpqNsVu+01d3/4HeIM611h4UzTKXy6xw+K5CRP3rKgIzEIgsg0q0Qxe+aXMJ4tUsxUncbXNca2bHdvY7Rn5rGL2vFQWF2PhEUiEuS4INRlA/rT6vnS8Nv3u5/syR8fY9RJ8auHDf/0yZFAfNwNXy5cr22RmW/3I+9MDdWf0bnn+1Opvji23CwVr5qFb60GvtONl43bxaev/OTtbL+fU5l8OZH78xHwzX7gZqn4pdMK+QY/jzpL/srDjv/8+cvLjYof8NqBRlt/EWMDmRmSoKfnlb+F2HEW38Y2K863BpVXSOMoYiS5gLFbZFYkN6Fzh6lbTLviOH6bSgmUeEa1HQghkZLF5UDtN4gmI+LMdtXx8v4lTBp7taIskc4aTHPEnTUprtDhCjd7/PJV5uMZpQrb7Y5nzxeshPuX77NqKvwiaVaSeVZMh4CuBYiZvt3QtSuGMPPkpmGKjuF+T54VytT064aHVxPdCmTSNJVknCdsp9lsN+x2PSVVeL9Q2Zqqq6nGW5Y506ovc16umY4nYggIqXHK02iD54Q2Z1Kw2PYaUQRX7xTCvOeXfvFtvv3hHdebhYePFNura5J/yZgOrDdrllRIITIfj2hZ87Vff4sPPryFYaYQHlOkksHKDhLkpcZHjVQj61XD7BXTPBF8JKhCs8kkF7H1iZjPKH1FZTLCa3Ie8CeByI8Bgu08MRU27TNO5yPtKpFSR21q3DIRxeOWvL+2zMcNFkcqmaa7ZjqdyHJL07Y4eSKWDFkilaSqKoxVpCQowjOOC1XXUVmJEvJxareSaCM5xhmFpukM2yc3bK46Uolsn61Zxsf3RBmJNDDHA2GumEVF/+TLLC/PjOOJIheKWZAukmP9+BpkjQ8JhkgJkphnwpKIBVSVWfXXjLeRxAPuNDGnIzEbytQgVxEfEkY3yBZGN1I1hdO9p+QV3S6hzIg/tNTmirpacB7W15pCIMYTIo8Q14gVlFNCSc8yK8b7mlIEq65CXfL3fypJ4Ne/vy3lT7BnKAJELvza9V/hN/7D/4j/y//5P+aDd3/nEmi8oXf7iRfnha8M3etd9MXj4UeTFF8eOm6WihfNwnTp/nXxKRM8nmK+aTvbH+RlvmxwPwFJFP6rmztOJvBXH3boIl47AJwvTTx+qNcONOK4EM4tvnhMfQ+poa46Yiq4tMEvAdODn0cIkrq9Iss9wUNeZmrrCEHS2A0PL+6pt09xasKf96zXPaNUiCypxExlLAWQ7Z67h5FWPWOovk5aVmSZeL75Aqf5gVgC73zpSyzDSJYCVWXWteI8nliJkavrzNntsaEg4g05zNjuGhdGFnfk+VWFlAlbS/y8ILTkrS8+Y38+YiiMy4GUNV5l1jtLSAP725GwRIzWVFc76qpmPifOd3dsbtZgCu58i4iCWmeWGOjqik3/JVCFUs68Or0EActtpuKGvFXU9WM6z+pGUOKR/W3h6foJOT6wsmu06ZmSolI9737nI/rVxPrqhmEeOH2UqIPjrS98lbP5Ll3bst8PxBLAKkx1BelEEQFURuiB7BNV47C1pNKgV5E8S5bFoaqACokSFTEnqBNGeWywxKTQsqKyif3dEV0LzmdFY0BXM8dZUZioZIZly7Q/w6YQgyPNgqqpWa0qhtsZN8xkISiTwftEzAWr1vjzzDJGQizUEmpdgxT4eaEIx3gbMbWhUpZligTnOe9ntKowukYqDSoQS0SVAM6w7q4RNmBiw7ZpeTW8i60kuqop2WPo2d9niLdoW5O6BYqhrXvGuxPaXCGrEx9+Y2J8JXn28xq5ntg8XxDScbx3aHXNuF9YkkM0FmNbbDMjNQgZOZ4SMlpykqzWhZIN0irGcaJuJD4sGLFCb+4RJaEUhAJuTkwuY4TmcK8RXPJwf1qJjz9+0laH36vNqOqet772N6nrSz3OT6a88aZLfO8/5fs+v7j4lD1dKn5u6P5cpxlwmaHxiRGPbbN/f3vkYD1//f6KK2//zJ/PoCO/uzte2tz+EKKUcgmCLy4uLi4uLi4uLi4+UT95xeLFxcXFxcXFxcXFxcWPcAk0Li4uLi4uLi4uLi4+cZdA4+Li4uLi4uLi4uLiE3cJNC4uLi4uLi4uLi4uPnGXQOPi4uLi4uLi4uLi4hN3CTQuLi4uLi4uLi4uLj5xl0Dj4uLi4uLi4uLi4uITdwk0Li4uLi4uLi4uLi4+cZdA4+Li4uLi4uLi4uLiE/f/B6pD1raBQOdUAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1000x1000 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "import numpy as np\n",
+    "from PIL import Image\n",
+    "\n",
+    "from perceptionmetrics.utils import conversion as uc\n",
+    "\n",
+    "image_fname = dataset.dataset[\"image\"].iloc[0]\n",
+    "image = Image.open(image_fname)\n",
+    "\n",
+    "label_fname = dataset.dataset[\"label\"].iloc[0]\n",
+    "label = Image.open(label_fname)\n",
+    "label = uc.label_to_rgb(label, dataset.ontology)\n",
+    "\n",
+    "pred = model.predict(image)\n",
+    "pred = uc.label_to_rgb(pred, model.ontology)\n",
+    "pred = pred.resize(label.size)\n",
+    "\n",
+    "plt.figure(figsize=(10, 10))\n",
+    "plt.subplot(131), plt.title(\"Image\"), plt.imshow(np.array(image)), plt.axis(\"off\")\n",
+    "plt.subplot(132), plt.title(\"Prediction\"), plt.imshow(np.array(pred)), plt.axis(\"off\")\n",
+    "plt.subplot(133), plt.title(\"GT\"), plt.imshow(np.array(label)), plt.axis(\"off\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1672/1672 [05:09<00:00,  5.40it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>void</th>\n",
+       "      <th>dirt</th>\n",
+       "      <th>grass</th>\n",
+       "      <th>tree</th>\n",
+       "      <th>pole</th>\n",
+       "      <th>water</th>\n",
+       "      <th>sky</th>\n",
+       "      <th>vehicle</th>\n",
+       "      <th>object</th>\n",
+       "      <th>asphalt</th>\n",
+       "      <th>...</th>\n",
+       "      <th>log</th>\n",
+       "      <th>person</th>\n",
+       "      <th>fence</th>\n",
+       "      <th>bush</th>\n",
+       "      <th>concrete</th>\n",
+       "      <th>barrier</th>\n",
+       "      <th>puddle</th>\n",
+       "      <th>mud</th>\n",
+       "      <th>rubble</th>\n",
+       "      <th>global</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>iou</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.830637</td>\n",
+       "      <td>0.676358</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.949209</td>\n",
+       "      <td>0.000099</td>\n",
+       "      <td>0.054734</td>\n",
+       "      <td>0.010433</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.243223</td>\n",
+       "      <td>0.034028</td>\n",
+       "      <td>0.591416</td>\n",
+       "      <td>0.619029</td>\n",
+       "      <td>0.049902</td>\n",
+       "      <td>0.342479</td>\n",
+       "      <td>0.165755</td>\n",
+       "      <td>0.081605</td>\n",
+       "      <td>0.232445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>acc</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.956694</td>\n",
+       "      <td>0.852252</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.978539</td>\n",
+       "      <td>0.000134</td>\n",
+       "      <td>0.076466</td>\n",
+       "      <td>0.041492</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.870784</td>\n",
+       "      <td>0.023046</td>\n",
+       "      <td>0.734917</td>\n",
+       "      <td>0.809408</td>\n",
+       "      <td>0.265849</td>\n",
+       "      <td>0.756522</td>\n",
+       "      <td>0.250438</td>\n",
+       "      <td>0.165216</td>\n",
+       "      <td>0.895927</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     void  dirt     grass      tree  pole  water       sky   vehicle  \\\n",
+       "iou   0.0   0.0  0.830637  0.676358   0.0    0.0  0.949209  0.000099   \n",
+       "acc   0.0   0.0  0.956694  0.852252   0.0    0.0  0.978539  0.000134   \n",
+       "\n",
+       "       object   asphalt  ...  log    person     fence      bush  concrete  \\\n",
+       "iou  0.054734  0.010433  ...  0.0  0.243223  0.034028  0.591416  0.619029   \n",
+       "acc  0.076466  0.041492  ...  0.0  0.870784  0.023046  0.734917  0.809408   \n",
+       "\n",
+       "      barrier    puddle       mud    rubble    global  \n",
+       "iou  0.049902  0.342479  0.165755  0.081605  0.232445  \n",
+       "acc  0.265849  0.756522  0.250438  0.165216  0.895927  \n",
+       "\n",
+       "[2 rows x 21 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results = model.eval(dataset, split=\"test\")\n",
+    "display(results)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 1d4fa7f4da4810065f78119988e9ddea8c077f5c Mon Sep 17 00:00:00 2001
From: Ioana Calangiu <calangiu@ini.uzh.ch>
Date: Tue, 14 Jul 2026 13:26:38 +0200
Subject: [PATCH 2/2] updated ids

---
 .../tutorial_image_segmentation_sample.ipynb  | 1114 ++++++++++++++---
 1 file changed, 952 insertions(+), 162 deletions(-)

diff --git a/examples/tutorial_image_segmentation_sample.ipynb b/examples/tutorial_image_segmentation_sample.ipynb
index 7d42194f..e621ff01 100644
--- a/examples/tutorial_image_segmentation_sample.ipynb
+++ b/examples/tutorial_image_segmentation_sample.ipynb
@@ -12,7 +12,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Download required data"
+    "## Download required data (SUB-SAMPLE - only 20 images for fast first run)\n",
+    "\n",
+    "tutorial_image_segmentation_sample.ipynb contains the links to the larger RELLIS-3D dataset (approx. 10 GB)"
    ]
   },
   {
@@ -33,29 +35,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Collecting gdown\n",
-      "  Downloading gdown-6.1.0-py3-none-any.whl.metadata (8.3 kB)\n",
-      "Collecting beautifulsoup4 (from gdown)\n",
-      "  Downloading beautifulsoup4-4.15.0-py3-none-any.whl.metadata (3.8 kB)\n",
+      "Requirement already satisfied: gdown in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (6.1.0)\n",
+      "Requirement already satisfied: beautifulsoup4 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from gdown) (4.15.0)\n",
       "Requirement already satisfied: filelock in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from gdown) (3.13.1)\n",
       "Requirement already satisfied: requests[socks] in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from gdown) (2.32.3)\n",
       "Requirement already satisfied: tqdm in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from gdown) (4.66.4)\n",
-      "Collecting soupsieve>=1.6.1 (from beautifulsoup4->gdown)\n",
-      "  Downloading soupsieve-2.8.4-py3-none-any.whl.metadata (4.6 kB)\n",
+      "Requirement already satisfied: soupsieve>=1.6.1 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from beautifulsoup4->gdown) (2.8.4)\n",
       "Requirement already satisfied: typing-extensions>=4.0.0 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from beautifulsoup4->gdown) (4.12.2)\n",
       "Requirement already satisfied: charset-normalizer<4,>=2 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from requests[socks]->gdown) (3.3.2)\n",
       "Requirement already satisfied: idna<4,>=2.5 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from requests[socks]->gdown) (3.6)\n",
       "Requirement already satisfied: urllib3<3,>=1.21.1 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from requests[socks]->gdown) (2.2.1)\n",
       "Requirement already satisfied: certifi>=2017.4.17 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from requests[socks]->gdown) (2024.6.2)\n",
-      "Collecting PySocks!=1.5.7,>=1.5.6 (from requests[socks]->gdown)\n",
-      "  Using cached PySocks-1.7.1-py3-none-any.whl.metadata (13 kB)\n",
-      "Downloading gdown-6.1.0-py3-none-any.whl (19 kB)\n",
-      "Downloading beautifulsoup4-4.15.0-py3-none-any.whl (109 kB)\n",
-      "Downloading soupsieve-2.8.4-py3-none-any.whl (37 kB)\n",
-      "Using cached PySocks-1.7.1-py3-none-any.whl (16 kB)\n",
-      "Installing collected packages: soupsieve, PySocks, beautifulsoup4, gdown\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4/4\u001b[0m [gdown]32m2/4\u001b[0m [beautifulsoup4]\n",
-      "\u001b[1A\u001b[2KSuccessfully installed PySocks-1.7.1 beautifulsoup4-4.15.0 gdown-6.1.0 soupsieve-2.8.4\n",
+      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from requests[socks]->gdown) (1.7.1)\n",
       "\n",
       "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m26.1.2\u001b[0m\n",
       "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
@@ -68,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -76,21 +67,21 @@
      "output_type": "stream",
      "text": [
       "Downloading...\n",
-      "From: https://drive.google.com/uc?id=1kV6_3NblB8JguleWJn329jrCIsNc6kQn\n",
-      "To: /Users/ioanacalangiu/Documents/GitHub/PerceptionMetrics/examples/local/data/Rellis_3D_image_split.zip\n",
-      "100%|██████████████████████████████████████████| 859/859 [00:00<00:00, 1.36MB/s]\n",
+      "From: https://drive.google.com/uc?id=1zI5yeKeOptwUzSQc1w1_uaDwe3vghwqS\n",
+      "To: /Users/ioanacalangiu/Documents/GitHub/PerceptionMetrics/examples/local/data_subsample/Rellis_3D_image_split.zip\n",
+      "100%|███████████████████████████████████████████| 859/859 [00:00<00:00, 741kB/s]\n",
       "Downloading...\n",
-      "From: https://drive.google.com/uc?id=16KZl2eFUKG8fzRou-pOKm_a-fgUpqa_U\n",
-      "To: /Users/ioanacalangiu/Documents/GitHub/PerceptionMetrics/examples/local/data/Rellis_3D_ontology.zip\n",
-      "100%|██████████████████████████████████████| 18.4k/18.4k [00:00<00:00, 12.2MB/s]\n",
+      "From: https://drive.google.com/uc?id=1tTUbKHU90KE2r2iDjRqW0RaVR4vzXKgy\n",
+      "To: /Users/ioanacalangiu/Documents/GitHub/PerceptionMetrics/examples/local/data_subsample/Rellis_3D_ontology.zip\n",
+      "100%|██████████████████████████████████████| 18.4k/18.4k [00:00<00:00, 13.0MB/s]\n",
       "Downloading...\n",
-      "From: https://drive.google.com/uc?id=1De9WamNV0LclToP7ajtHhhiT7NFHW4Z7\n",
-      "To: /Users/ioanacalangiu/Documents/GitHub/PerceptionMetrics/examples/local/data/Rellis_3D_pylon_camera_node_label_id.zip\n",
-      "100%|████████████████████████████████████████| 454k/454k [00:00<00:00, 16.4MB/s]\n",
+      "From: https://drive.google.com/uc?id=1-xUv_Xmg93BjJ94XCNJMt6SvkTtLNA9D\n",
+      "To: /Users/ioanacalangiu/Documents/GitHub/PerceptionMetrics/examples/local/data_subsample/Rellis_3D_pylon_camera_node_label_id.zip\n",
+      "100%|████████████████████████████████████████| 454k/454k [00:00<00:00, 19.8MB/s]\n",
       "Downloading...\n",
-      "From: https://drive.google.com/uc?id=1Xuvi8AzI94ybe7hvWdbyqxkiCNoU4elj\n",
-      "To: /Users/ioanacalangiu/Documents/GitHub/PerceptionMetrics/examples/local/data/Rellis_3D_pylon_camera_node.zip\n",
-      "100%|██████████████████████████████████████| 23.4M/23.4M [00:00<00:00, 27.5MB/s]\n"
+      "From: https://drive.google.com/uc?id=1gAksuWfWplOQLDZ_bMXp7k5t5uHeYO8x\n",
+      "To: /Users/ioanacalangiu/Documents/GitHub/PerceptionMetrics/examples/local/data_subsample/Rellis_3D_pylon_camera_node.zip\n",
+      "100%|██████████████████████████████████████| 23.4M/23.4M [00:00<00:00, 44.8MB/s]\n"
      ]
     }
    ],
@@ -98,83 +89,80 @@
     "!mkdir -p local/data_subsample && cd local/data_subsample\n",
     "\n",
     "# Download\n",
-    "!gdown 1kV6_3NblB8JguleWJn329jrCIsNc6kQn -O local/data_subsample/  # split\n",
-    "!gdown 16KZl2eFUKG8fzRou-pOKm_a-fgUpqa_U -O local/data_subsample/  # ontology\n",
-    "!gdown 1De9WamNV0LclToP7ajtHhhiT7NFHW4Z7 -O local/data_subsample/  # labels\n",
-    "!gdown 1Xuvi8AzI94ybe7hvWdbyqxkiCNoU4elj -O local/data_subsample/  # images"
+    "!gdown 1zI5yeKeOptwUzSQc1w1_uaDwe3vghwqS -O local/data_subsample/  # split\n",
+    "!gdown 1tTUbKHU90KE2r2iDjRqW0RaVR4vzXKgy -O local/data_subsample/  # ontology\n",
+    "!gdown 1-xUv_Xmg93BjJ94XCNJMt6SvkTtLNA9D -O local/data_subsample/  # labels\n",
+    "!gdown 1gAksuWfWplOQLDZ_bMXp7k5t5uHeYO8x -O local/data_subsample/  # images"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Archive:  local/data/Rellis_3D_image_split.zip\n",
-      "  inflating: local/data/rellis3d/val.lst  \n",
-      "  inflating: local/data/rellis3d/test.lst  \n",
-      "  inflating: local/data/rellis3d/train.lst  \n",
-      "Archive:  local/data/Rellis_3D_ontology.zip\n",
-      "   creating: local/data/rellis3d/Rellis_3D_ontology/\n",
-      "  inflating: local/data/rellis3d/Rellis_3D_ontology/ontology.yaml  \n",
-      "  inflating: local/data/rellis3d/Rellis_3D_ontology/ontology.csv  \n",
-      "  inflating: local/data/rellis3d/Rellis_3D_ontology/ontology.png  \n",
-      "Archive:  local/data/Rellis_3D_pylon_camera_node_label_id.zip\n",
-      "   creating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/\n",
-      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000314-1581624684_149.png  \n",
-      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame001709-1581624823_650.png  \n",
-      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000326-1581624685_350.png  \n",
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-      "   creating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/\n",
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-      "  inflating: local/data/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000004-1581624653_150.jpg  \n"
+      "Archive:  local/data_subsample/Rellis_3D_image_split.zip\n",
+      "  inflating: local/data_subsample/rellis3d/val.lst  \n",
+      "  inflating: local/data_subsample/rellis3d/test.lst  \n",
+      "  inflating: local/data_subsample/rellis3d/train.lst  \n",
+      "Archive:  local/data_subsample/Rellis_3D_ontology.zip\n",
+      "  inflating: local/data_subsample/rellis3d/Rellis_3D_ontology/ontology.yaml  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis_3D_ontology/ontology.csv  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis_3D_ontology/ontology.png  \n",
+      "Archive:  local/data_subsample/Rellis_3D_pylon_camera_node_label_id.zip\n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000314-1581624684_149.png  \n",
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+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000322-1581624684_949.png  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000308-1581624683_550.png  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000008-1581624653_549.png  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000312-1581624683_950.png  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000324-1581624685_150.png  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000006-1581624653_349.png  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000310-1581624683_750.png  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node_label_id/frame000004-1581624653_150.png  \n",
+      "Archive:  local/data_subsample/Rellis_3D_pylon_camera_node.zip\n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000314-1581624684_149.jpg  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node/frame001709-1581624823_650.jpg  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000326-1581624685_350.jpg  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000318-1581624684_550.jpg  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node/frame001707-1581624823_450.jpg  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000316-1581624684_350.jpg  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node/frame001713-1581624824_050.jpg  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node/frame001711-1581624823_849.jpg  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node/frame001715-1581624824_250.jpg  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000000-1581624652_750.jpg  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000002-1581624652_949.jpg  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000320-1581624684_749.jpg  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000322-1581624684_949.jpg  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000308-1581624683_550.jpg  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000008-1581624653_549.jpg  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000324-1581624685_150.jpg  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000312-1581624683_950.jpg  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000006-1581624653_349.jpg  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000310-1581624683_750.jpg  \n",
+      "  inflating: local/data_subsample/rellis3d/Rellis-3D/00000/pylon_camera_node/frame000004-1581624653_150.jpg  \n"
      ]
     }
    ],
    "source": [
     "# Unzip\n",
-    "!unzip -o local/data_subsample/Rellis_3D_image_split.zip -d local/data/rellis3d/\n",
-    "!unzip -o local/data_subsample/Rellis_3D_ontology.zip -d local/data/rellis3d/\n",
-    "!unzip -o local/data_subsample/Rellis_3D_pylon_camera_node_label_id.zip -d local/data/rellis3d/\n",
-    "!unzip -o local/data_subsample/Rellis_3D_pylon_camera_node.zip -d local/data/rellis3d/"
+    "!unzip -o local/data_subsample/Rellis_3D_image_split.zip -d local/data_subsample/rellis3d/\n",
+    "!unzip -o local/data_subsample/Rellis_3D_ontology.zip -d local/data_subsample/rellis3d/\n",
+    "!unzip -o local/data_subsample/Rellis_3D_pylon_camera_node_label_id.zip -d local/data_subsample/rellis3d/\n",
+    "!unzip -o local/data_subsample/Rellis_3D_pylon_camera_node.zip -d local/data_subsample/rellis3d/"
    ]
   },
   {
@@ -186,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -194,26 +182,26 @@
      "output_type": "stream",
      "text": [
       "Downloading...\n",
-      "From (original): https://drive.google.com/uc?id=1TS04wMOrfoAogVCbItsz0UGG-t3qu7g4\n",
-      "From (redirected): https://drive.google.com/uc?id=1TS04wMOrfoAogVCbItsz0UGG-t3qu7g4&confirm=t&uuid=8ff3937a-b000-4446-8962-d626e2ee21c4\n",
-      "To: /Users/ioanacalangiu/Documents/GitHub/PerceptionMetrics/examples/local/data/image_segmentation_model.pth\n",
-      "100%|████████████████████████████████████████| 159M/159M [00:08<00:00, 19.0MB/s]\n",
+      "From (original): https://drive.google.com/uc?id=1Uqy4BU_-gwzeRRmo-_jTtYNDV88Sick-\n",
+      "From (redirected): https://drive.google.com/uc?id=1Uqy4BU_-gwzeRRmo-_jTtYNDV88Sick-&confirm=t&uuid=7a5d9f07-4b91-4d0f-92b3-db0a1517eb29\n",
+      "To: /Users/ioanacalangiu/Documents/GitHub/PerceptionMetrics/examples/local/data_subsample/image_segmentation_model.pth\n",
+      "100%|████████████████████████████████████████| 159M/159M [00:02<00:00, 73.4MB/s]\n",
       "Downloading...\n",
-      "From: https://drive.google.com/uc?id=1UtVB7SN-0fE6eJLbSZHeRUJD26vJFZhK\n",
-      "To: /Users/ioanacalangiu/Documents/GitHub/PerceptionMetrics/examples/local/data/image_segmentation_model_cfg.json\n",
-      "100%|███████████████████████████████████████████| 288/288 [00:00<00:00, 419kB/s]\n",
+      "From: https://drive.google.com/uc?id=1suSJOahLq13SCZ3XJ3mghQIZAKkdG5E1\n",
+      "To: /Users/ioanacalangiu/Documents/GitHub/PerceptionMetrics/examples/local/data_subsample/image_segmentation_model_cfg.json\n",
+      "100%|███████████████████████████████████████████| 288/288 [00:00<00:00, 670kB/s]\n",
       "Downloading...\n",
-      "From: https://drive.google.com/uc?id=1Qgoq3BlopB_zMVMbPGGlEc52aCwRZ9_n\n",
-      "To: /Users/ioanacalangiu/Documents/GitHub/PerceptionMetrics/examples/local/data/image_segmentation_model_ontology.json\n",
-      "100%|██████████████████████████████████████| 2.31k/2.31k [00:00<00:00, 4.00MB/s]\n"
+      "From: https://drive.google.com/uc?id=19cRKEL72uuyRRmxPw5ev68PvCx0bBfIy\n",
+      "To: /Users/ioanacalangiu/Documents/GitHub/PerceptionMetrics/examples/local/data_subsample/image_segmentation_model_ontology.json\n",
+      "100%|██████████████████████████████████████| 2.31k/2.31k [00:00<00:00, 3.72MB/s]\n"
      ]
     }
    ],
    "source": [
     "# Download\n",
-    "!gdown 1TS04wMOrfoAogVCbItsz0UGG-t3qu7g4 -O local/data_subsample/image_segmentation_model.pth            # model\n",
-    "!gdown 1UtVB7SN-0fE6eJLbSZHeRUJD26vJFZhK -O local/data_subsample/image_segmentation_model_cfg.json       # configuration\n",
-    "!gdown 1Qgoq3BlopB_zMVMbPGGlEc52aCwRZ9_n -O local/data_subsample/image_segmentation_model_ontology.json  # ontology"
+    "!gdown 1Uqy4BU_-gwzeRRmo-_jTtYNDV88Sick- -O local/data_subsample/image_segmentation_model.pth            # model\n",
+    "!gdown 1suSJOahLq13SCZ3XJ3mghQIZAKkdG5E1 -O local/data_subsample/image_segmentation_model_cfg.json       # configuration\n",
+    "!gdown 19cRKEL72uuyRRmxPw5ev68PvCx0bBfIy -O local/data_subsample/image_segmentation_model_ontology.json  # ontology"
    ]
   },
   {
@@ -225,18 +213,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'perceptionmetrics'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mperceptionmetrics\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdatasets\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Rellis3DImageSegmentationDataset\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mperceptionmetrics\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mmodels\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m TorchImageSegmentationModel\n\u001b[32m      4\u001b[39m dataset = Rellis3DImageSegmentationDataset(\n\u001b[32m      5\u001b[39m     dataset_dir=\u001b[33m\"\u001b[39m\u001b[33mlocal/data/rellis3d/Rellis-3D\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m      6\u001b[39m     split_dir=\u001b[33m\"\u001b[39m\u001b[33mlocal/data/rellis3d\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m      7\u001b[39m     ontology_fname=\u001b[33m\"\u001b[39m\u001b[33mlocal/data/rellis3d/Rellis_3D_ontology/ontology.yaml\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m      8\u001b[39m )\n",
-      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'perceptionmetrics'"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-07-14 13:17:10.040091: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Samples retrieved: 20 / 20\n"
      ]
     }
    ],
@@ -266,12 +258,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x1000 with 3 Axes>"
       ]
@@ -314,14 +306,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 1672/1672 [05:09<00:00,  5.40it/s]\n"
+      "100%|██████████| 5/5 [01:16<00:00, 15.40s/it]\n",
+      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/perceptionmetrics/utils/segmentation_metrics.py:147: RuntimeWarning: invalid value encountered in divide\n",
+      "  return np.where(denominator > 0, tp / denominator, np.nan)\n",
+      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/perceptionmetrics/utils/segmentation_metrics.py:164: RuntimeWarning: invalid value encountered in divide\n",
+      "  return np.where(denominator > 0, tp / denominator, np.nan)\n",
+      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/perceptionmetrics/utils/segmentation_metrics.py:258: RuntimeWarning: Mean of empty slice\n",
+      "  return float(np.nanmean(metric(per_class=True)))\n",
+      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/perceptionmetrics/utils/segmentation_metrics.py:222: RuntimeWarning: invalid value encountered in divide\n",
+      "  return np.where(union > 0, tp / union, np.nan)\n",
+      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/perceptionmetrics/utils/segmentation_metrics.py:240: RuntimeWarning: invalid value encountered in divide\n",
+      "  return np.where(denominator > 0, 2 * tp / denominator, np.nan)\n"
      ]
     },
     {
@@ -356,7 +358,6 @@
        "      <th>object</th>\n",
        "      <th>asphalt</th>\n",
        "      <th>...</th>\n",
-       "      <th>log</th>\n",
        "      <th>person</th>\n",
        "      <th>fence</th>\n",
        "      <th>bush</th>\n",
@@ -365,77 +366,866 @@
        "      <th>puddle</th>\n",
        "      <th>mud</th>\n",
        "      <th>rubble</th>\n",
-       "      <th>global</th>\n",
+       "      <th>macro</th>\n",
+       "      <th>micro</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
+       "      <th>tp</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fp</th>\n",
+       "      <td>421340.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fn</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>95531.000000</td>\n",
+       "      <td>103427.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>112198.000000</td>\n",
+       "      <td>48.000000</td>\n",
+       "      <td>1521.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1342.000000</td>\n",
+       "      <td>107273.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tn</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>421340.0</td>\n",
+       "      <td>421340.0</td>\n",
+       "      <td>325809.000000</td>\n",
+       "      <td>317913.000000</td>\n",
+       "      <td>421340.0</td>\n",
+       "      <td>421340.0</td>\n",
+       "      <td>309142.000000</td>\n",
+       "      <td>421292.000000</td>\n",
+       "      <td>419819.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>421340.0</td>\n",
+       "      <td>421340.0</td>\n",
+       "      <td>421340.0</td>\n",
+       "      <td>421340.0</td>\n",
+       "      <td>421340.0</td>\n",
+       "      <td>421340.0</td>\n",
+       "      <td>419998.000000</td>\n",
+       "      <td>314067.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>precision</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>recall</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.773269</td>\n",
+       "      <td>0.754528</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.733711</td>\n",
+       "      <td>0.999886</td>\n",
+       "      <td>0.99639</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.996815</td>\n",
+       "      <td>0.7454</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f1_score</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>iou</th>\n",
        "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dice_score</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>void</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>95531.000000</td>\n",
+       "      <td>103427.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>112198.000000</td>\n",
+       "      <td>48.000000</td>\n",
+       "      <td>1521.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1342.000000</td>\n",
+       "      <td>107273.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dirt</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>grass</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tree</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pole</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>water</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sky</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.830637</td>\n",
-       "      <td>0.676358</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.949209</td>\n",
-       "      <td>0.000099</td>\n",
-       "      <td>0.054734</td>\n",
-       "      <td>0.010433</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vehicle</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>object</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>asphalt</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>building</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>log</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>person</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fence</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
        "      <td>...</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.243223</td>\n",
-       "      <td>0.034028</td>\n",
-       "      <td>0.591416</td>\n",
-       "      <td>0.619029</td>\n",
-       "      <td>0.049902</td>\n",
-       "      <td>0.342479</td>\n",
-       "      <td>0.165755</td>\n",
-       "      <td>0.081605</td>\n",
-       "      <td>0.232445</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>acc</th>\n",
+       "      <th>bush</th>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.956694</td>\n",
-       "      <td>0.852252</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.978539</td>\n",
-       "      <td>0.000134</td>\n",
-       "      <td>0.076466</td>\n",
-       "      <td>0.041492</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
        "      <td>...</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.870784</td>\n",
-       "      <td>0.023046</td>\n",
-       "      <td>0.734917</td>\n",
-       "      <td>0.809408</td>\n",
-       "      <td>0.265849</td>\n",
-       "      <td>0.756522</td>\n",
-       "      <td>0.250438</td>\n",
-       "      <td>0.165216</td>\n",
-       "      <td>0.895927</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>concrete</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>barrier</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>puddle</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mud</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rubble</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>2 rows × 21 columns</p>\n",
+       "<p>30 rows × 22 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "     void  dirt     grass      tree  pole  water       sky   vehicle  \\\n",
-       "iou   0.0   0.0  0.830637  0.676358   0.0    0.0  0.949209  0.000099   \n",
-       "acc   0.0   0.0  0.956694  0.852252   0.0    0.0  0.978539  0.000134   \n",
+       "                void      dirt     grass           tree           pole  \\\n",
+       "tp               0.0       0.0       0.0       0.000000       0.000000   \n",
+       "fp          421340.0       0.0       0.0       0.000000       0.000000   \n",
+       "fn               0.0       0.0       0.0   95531.000000  103427.000000   \n",
+       "tn               0.0  421340.0  421340.0  325809.000000  317913.000000   \n",
+       "precision        0.0       NaN       NaN            NaN            NaN   \n",
+       "recall           NaN       NaN       NaN       0.000000       0.000000   \n",
+       "accuracy         0.0       1.0       1.0       0.773269       0.754528   \n",
+       "f1_score         NaN       NaN       NaN            NaN            NaN   \n",
+       "iou              0.0       NaN       NaN       0.000000       0.000000   \n",
+       "dice_score       0.0       NaN       NaN       0.000000       0.000000   \n",
+       "void             0.0       0.0       0.0   95531.000000  103427.000000   \n",
+       "dirt             0.0       0.0       0.0       0.000000       0.000000   \n",
+       "grass            0.0       0.0       0.0       0.000000       0.000000   \n",
+       "tree             0.0       0.0       0.0       0.000000       0.000000   \n",
+       "pole             0.0       0.0       0.0       0.000000       0.000000   \n",
+       "water            0.0       0.0       0.0       0.000000       0.000000   \n",
+       "sky              0.0       0.0       0.0       0.000000       0.000000   \n",
+       "vehicle          0.0       0.0       0.0       0.000000       0.000000   \n",
+       "object           0.0       0.0       0.0       0.000000       0.000000   \n",
+       "asphalt          0.0       0.0       0.0       0.000000       0.000000   \n",
+       "building         0.0       0.0       0.0       0.000000       0.000000   \n",
+       "log              0.0       0.0       0.0       0.000000       0.000000   \n",
+       "person           0.0       0.0       0.0       0.000000       0.000000   \n",
+       "fence            0.0       0.0       0.0       0.000000       0.000000   \n",
+       "bush             0.0       0.0       0.0       0.000000       0.000000   \n",
+       "concrete         0.0       0.0       0.0       0.000000       0.000000   \n",
+       "barrier          0.0       0.0       0.0       0.000000       0.000000   \n",
+       "puddle           0.0       0.0       0.0       0.000000       0.000000   \n",
+       "mud              0.0       0.0       0.0       0.000000       0.000000   \n",
+       "rubble           0.0       0.0       0.0       0.000000       0.000000   \n",
+       "\n",
+       "               water       sky        vehicle         object       asphalt  \\\n",
+       "tp               0.0       0.0       0.000000       0.000000       0.00000   \n",
+       "fp               0.0       0.0       0.000000       0.000000       0.00000   \n",
+       "fn               0.0       0.0  112198.000000      48.000000    1521.00000   \n",
+       "tn          421340.0  421340.0  309142.000000  421292.000000  419819.00000   \n",
+       "precision        NaN       NaN            NaN            NaN           NaN   \n",
+       "recall           NaN       NaN       0.000000       0.000000       0.00000   \n",
+       "accuracy         1.0       1.0       0.733711       0.999886       0.99639   \n",
+       "f1_score         NaN       NaN            NaN            NaN           NaN   \n",
+       "iou              NaN       NaN       0.000000       0.000000       0.00000   \n",
+       "dice_score       NaN       NaN       0.000000       0.000000       0.00000   \n",
+       "void             0.0       0.0  112198.000000      48.000000    1521.00000   \n",
+       "dirt             0.0       0.0       0.000000       0.000000       0.00000   \n",
+       "grass            0.0       0.0       0.000000       0.000000       0.00000   \n",
+       "tree             0.0       0.0       0.000000       0.000000       0.00000   \n",
+       "pole             0.0       0.0       0.000000       0.000000       0.00000   \n",
+       "water            0.0       0.0       0.000000       0.000000       0.00000   \n",
+       "sky              0.0       0.0       0.000000       0.000000       0.00000   \n",
+       "vehicle          0.0       0.0       0.000000       0.000000       0.00000   \n",
+       "object           0.0       0.0       0.000000       0.000000       0.00000   \n",
+       "asphalt          0.0       0.0       0.000000       0.000000       0.00000   \n",
+       "building         0.0       0.0       0.000000       0.000000       0.00000   \n",
+       "log              0.0       0.0       0.000000       0.000000       0.00000   \n",
+       "person           0.0       0.0       0.000000       0.000000       0.00000   \n",
+       "fence            0.0       0.0       0.000000       0.000000       0.00000   \n",
+       "bush             0.0       0.0       0.000000       0.000000       0.00000   \n",
+       "concrete         0.0       0.0       0.000000       0.000000       0.00000   \n",
+       "barrier          0.0       0.0       0.000000       0.000000       0.00000   \n",
+       "puddle           0.0       0.0       0.000000       0.000000       0.00000   \n",
+       "mud              0.0       0.0       0.000000       0.000000       0.00000   \n",
+       "rubble           0.0       0.0       0.000000       0.000000       0.00000   \n",
        "\n",
-       "       object   asphalt  ...  log    person     fence      bush  concrete  \\\n",
-       "iou  0.054734  0.010433  ...  0.0  0.243223  0.034028  0.591416  0.619029   \n",
-       "acc  0.076466  0.041492  ...  0.0  0.870784  0.023046  0.734917  0.809408   \n",
+       "            ...    person     fence      bush  concrete   barrier    puddle  \\\n",
+       "tp          ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
+       "fp          ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
+       "fn          ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
+       "tn          ...  421340.0  421340.0  421340.0  421340.0  421340.0  421340.0   \n",
+       "precision   ...       NaN       NaN       NaN       NaN       NaN       NaN   \n",
+       "recall      ...       NaN       NaN       NaN       NaN       NaN       NaN   \n",
+       "accuracy    ...       1.0       1.0       1.0       1.0       1.0       1.0   \n",
+       "f1_score    ...       NaN       NaN       NaN       NaN       NaN       NaN   \n",
+       "iou         ...       NaN       NaN       NaN       NaN       NaN       NaN   \n",
+       "dice_score  ...       NaN       NaN       NaN       NaN       NaN       NaN   \n",
+       "void        ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
+       "dirt        ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
+       "grass       ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
+       "tree        ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
+       "pole        ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
+       "water       ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
+       "sky         ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
+       "vehicle     ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
+       "object      ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
+       "asphalt     ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
+       "building    ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
+       "log         ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
+       "person      ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
+       "fence       ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
+       "bush        ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
+       "concrete    ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
+       "barrier     ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
+       "puddle      ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
+       "mud         ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
+       "rubble      ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
        "\n",
-       "      barrier    puddle       mud    rubble    global  \n",
-       "iou  0.049902  0.342479  0.165755  0.081605  0.232445  \n",
-       "acc  0.265849  0.756522  0.250438  0.165216  0.895927  \n",
+       "                      mud       rubble  macro  micro  \n",
+       "tp               0.000000       0.0000    NaN    NaN  \n",
+       "fp               0.000000       0.0000    NaN    NaN  \n",
+       "fn            1342.000000  107273.0000    NaN    NaN  \n",
+       "tn          419998.000000  314067.0000    NaN    NaN  \n",
+       "precision             NaN          NaN    0.0    0.0  \n",
+       "recall           0.000000       0.0000    0.0    0.0  \n",
+       "accuracy         0.996815       0.7454    0.9    0.9  \n",
+       "f1_score              NaN          NaN    NaN    NaN  \n",
+       "iou              0.000000       0.0000    0.0    0.0  \n",
+       "dice_score       0.000000       0.0000    0.0    0.0  \n",
+       "void          1342.000000  107273.0000    NaN    NaN  \n",
+       "dirt             0.000000       0.0000    NaN    NaN  \n",
+       "grass            0.000000       0.0000    NaN    NaN  \n",
+       "tree             0.000000       0.0000    NaN    NaN  \n",
+       "pole             0.000000       0.0000    NaN    NaN  \n",
+       "water            0.000000       0.0000    NaN    NaN  \n",
+       "sky              0.000000       0.0000    NaN    NaN  \n",
+       "vehicle          0.000000       0.0000    NaN    NaN  \n",
+       "object           0.000000       0.0000    NaN    NaN  \n",
+       "asphalt          0.000000       0.0000    NaN    NaN  \n",
+       "building         0.000000       0.0000    NaN    NaN  \n",
+       "log              0.000000       0.0000    NaN    NaN  \n",
+       "person           0.000000       0.0000    NaN    NaN  \n",
+       "fence            0.000000       0.0000    NaN    NaN  \n",
+       "bush             0.000000       0.0000    NaN    NaN  \n",
+       "concrete         0.000000       0.0000    NaN    NaN  \n",
+       "barrier          0.000000       0.0000    NaN    NaN  \n",
+       "puddle           0.000000       0.0000    NaN    NaN  \n",
+       "mud              0.000000       0.0000    NaN    NaN  \n",
+       "rubble           0.000000       0.0000    NaN    NaN  \n",
        "\n",
-       "[2 rows x 21 columns]"
+       "[30 rows x 22 columns]"
       ]
      },
      "metadata": {},